{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T23:08:52Z","timestamp":1787612932997,"version":"build-2736575974"},"reference-count":164,"publisher":"e-Century Publishing Corporation","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Am J Transl Res"],"DOI":"10.62347\/xill3707","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T04:21:39Z","timestamp":1737519699000},"page":"28-46","source":"Crossref","is-referenced-by-count":16,"title":["Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review"],"prefix":"10.62347","volume":"17","author":[{"given":"Hugo Dugolin","family":"Ceccato","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"48694","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1038\/ng.3760","article-title":"Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease","volume":"49","author":"de Lange","year":"2017","unstructured":"de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, Jostins L, Rice DL, Gutierrez-Achury J, Ji SG, Heap G, Nimmo ER, Edwards C, Henderson P, Mowat C, Sanderson J, Satsangi J, Simmons A, Wilson DC, Tremelling M, Hart A, Mathew CG, Newman WG, Parkes M, Lees CW, Uhlig H, Hawkey C, Prescott NJ, Ahmad T, Mansfield JC, Anderson CA and Barrett JC. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet 2017; 49: 256-261.","journal-title":"Nat Genet"},{"key":"ref1","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1146\/annurev-med-042320-021020","article-title":"The gut microbiome and inflammatory bowel diseases","volume":"73","author":"Shan","year":"2022","unstructured":"Shan Y, Lee M and Chang EB. The gut microbiome and inflammatory bowel diseases. Annu Rev Med 2022; 73: 455-468.","journal-title":"Annu Rev Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1111\/jgh.12193","article-title":"Proteomics and metabolomics in inflammatory bowel disease","volume":"28","author":"Yau","year":"2013","unstructured":"Yau Y, Leong RW, Zeng M and Wasinger VC. Proteomics and metabolomics in inflammatory bowel disease. J Gastroenterol Hepatol 2013; 28: 1076-1086.","journal-title":"J Gastroenterol Hepatol"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"Cohen BL and Sachar DB. Update on anti-tumor necrosis factor agents and other new drugs for inflammatory bowel disease. BMJ 2017; 357: j2505.","DOI":"10.1136\/bmj.j2505"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"Bouguen G and Peyrin-Biroulet L. Surgery for adult Crohn’s disease: what is the actual risk? Gut 2011; 60: 1178-1181.","DOI":"10.1136\/gut.2010.234617"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1111\/j.1365-2036.2012.05002.x","article-title":"Review article: the natural history of postoperative Crohn’s disease recurrence","volume":"35","author":"Buisson","year":"2012","unstructured":"Buisson A, Chevaux JB, Allen PB, Bommelaer G and Peyrin-Biroulet L. Review article: the natural history of postoperative Crohn’s disease recurrence. Aliment Pharmacol Ther 2012; 35: 625-633.","journal-title":"Aliment Pharmacol Ther"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"Dasharathy SS, Limketkai BN and Sauk JS. What’s new in the postoperative management of Crohn’s disease? Dig Dis Sci 2022; 67: 3508-3517.","DOI":"10.1007\/s10620-021-07205-w"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1136\/jcp.2003.015214","article-title":"Indeterminate colitis","volume":"57","author":"Guindi","year":"2004","unstructured":"Guindi M and Riddell RH. Indeterminate colitis. J Clin Pathol 2004; 57: 1233-1244.","journal-title":"J Clin Pathol"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.crohns.2009.07.001","article-title":"Frequency of indeterminate colitis in children and adults with IBD - a metaanalysis","volume":"3","author":"Prenzel","year":"2009","unstructured":"Prenzel F and Uhlig HH. Frequency of indeterminate colitis in children and adults with IBD - a metaanalysis. J Crohns Colitis 2009; 3: 277-281.","journal-title":"J Crohns Colitis"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1038\/ajg.2008.38","article-title":"Influence of phenotype at diagnosis and of other potential prognostic factors on the course of inflammatory bowel disease","volume":"104","author":"Romberg-Camps MJ","year":"2009","unstructured":"Romberg-Camps MJ, Dagnelie PC, Kester AD, Hesselink-van de Kruijs MA, Cilissen M, Engels LG, Van Deursen C, Hameeteman WH, Wolters FL, Russel MG and Stockbrügger RW. Influence of phenotype at diagnosis and of other potential prognostic factors on the course of inflammatory bowel disease. Am J Gastroenterol 2009; 104: 371-383.","journal-title":"Am J Gastroenterol"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision medicine, ai, and the future of personalized health care","volume":"14","author":"Johnson","year":"2021","unstructured":"Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J and Snowdon JL. Precision medicine, ai, and the future of personalized health care. Clin Transl Sci 2021; 14: 86-93.","journal-title":"Clin Transl Sci"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"Borg-Bartolo SP, Boyapati RK, Satsangi J and Kalla R. Precision medicine in inflammatory bowel disease: concept, progress and challenges. F1000Res 2020; 9: F1000 Faculty Rev-54.","DOI":"10.12688\/f1000research.20928.1"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","year":"2017","unstructured":"Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H and Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230-243.","journal-title":"Stroke Vasc Neurol"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P and Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.","DOI":"10.1136\/bmj.n71"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan-a web and mobile app for systematic reviews","volume":"5","author":"Ouzzani","year":"2016","unstructured":"Ouzzani M, Hammady H, Fedorowicz Z and Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev 2016; 5: 210.","journal-title":"Syst Rev"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1136\/gutjnl-2020-321106","article-title":"Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study","volume":"70","author":"Clooney","year":"2021","unstructured":"Clooney AG, Eckenberger J, Laserna-Mendieta E, Sexton KA, Bernstein MT, Vagianos K, Sargent M, Ryan FJ, Moran C, Sheehan D, Sleator RD, Targownik LE, Bernstein CN, Shanahan F and Claesson MJ. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut 2021; 70: 499-510.","journal-title":"Gut"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.dld.2020.03.007","article-title":"The faecal scent of inflammatory bowel disease: detection and monitoring based on volatile organic compound analysis","volume":"52","author":"Bosch","year":"2020","unstructured":"Bosch S, Wintjens DSJ, Wicaksono A, Kuijvenhoven J, van der Hulst R, Stokkers P, Daulton E, Pierik MJ, Covington JA, de Meij TGJ and de Boer NKH. The faecal scent of inflammatory bowel disease: detection and monitoring based on volatile organic compound analysis. Dig Liver Dis 2020; 52: 745-752.","journal-title":"Dig Liver Dis"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1016\/j.csbj.2021.11.037","article-title":"Dysbiosis and relapse-related microbiome in inflammatory bowel disease: a shotgun metagenomic approach","volume":"19","author":"Serrano-G&#xF3","year":"2021","unstructured":"Serrano-Gómez G, Mayorga L, Oyarzun I, Roca J, Borruel N, Casellas F, Varela E, Pozuelo M, Machiels K, Guarner F, Vermeire S and Manichanh C. Dysbiosis and relapse-related microbiome in inflammatory bowel disease: a shotgun metagenomic approach. Comput Struct Biotechnol J 2021; 19: 6481-6489.","journal-title":"Comput Struct Biotechnol J"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"Sarrabayrouse G, Elias A, Yáñez F, Mayorga L, Varela E, Bartoli C, Casellas F, Borruel N, Herrera de Guise C, Machiels K, Vermeire S and Manichanh C. Fungal and bacterial loads: noninvasive inflammatory bowel disease biomarkers for the clinical setting. mSystems 2021; 6: e01277-20.","DOI":"10.1128\/msystems.01277-20"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"Zhou Y, Xu ZZ, He Y, Yang Y, Liu L, Lin Q, Nie Y, Li M, Zhi F, Liu S, Amir A, González A, Tripathi A, Chen M, Wu GD, Knight R, Zhou H and Chen Y. Gut microbiota offers universal biomarkers across ethnicity in inflammatory bowel disease diagnosis and infliximab response prediction. mSystems 2018; 3: e00188-17.","DOI":"10.1128\/msystems.00188-17"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1065297","DOI":"10.3389\/fgene.2022.1065297","article-title":"Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease","volume":"13","author":"Ye","year":"2022","unstructured":"Ye C, Zhu S, Gao Y and Huang Y. Landscape of sialylation patterns identify biomarkers for diagnosis and prediction of response to anti-TNF therapy in crohn’s disease. Front Genet 2022; 13: 1065297.","journal-title":"Front Genet"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s40168-018-0398-3","article-title":"Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease","volume":"6","author":"Douglas","year":"2018","unstructured":"Douglas GM, Hansen R, Jones CMA, Dunn KA, Comeau AM, Bielawski JP, Tayler R, El-Omar EM, Russell RK, Hold GL, Langille MGI and Van Limbergen J. Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease. Microbiome 2018; 6: 13.","journal-title":"Microbiome"},{"key":"ref22","doi-asserted-by":"publisher","unstructured":"Pei J, Wang G, Li Y, Li L, Li C, Wu Y, Liu J and Tian G. Utility of four machine learning approaches for identifying ulcerative colitis and Crohn’s disease. Heliyon 2023; 10: e23439.","DOI":"10.1016\/j.heliyon.2023.e23439"},{"key":"ref23","doi-asserted-by":"publisher","unstructured":"Montero-Meléndez T, Llor X, García-Planella E, Perretti M and Suárez A. Identification of novel predictor classifiers for inflammatory bowel disease by gene expression profiling. PLoS One 2013; 8: e76235.","DOI":"10.1371\/journal.pone.0076235"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1239496","DOI":"10.3389\/fimmu.2024.1239496","article-title":"Constructing models for Crohn’s disease diagnosis and prediction of infliximab non-response based on angiogenesis-related genes","volume":"15","author":"Zheng","year":"2024","unstructured":"Zheng C, Chen X, Ke Y, Xu X, Wu C and Jiang L. Constructing models for Crohn’s disease diagnosis and prediction of infliximab non-response based on angiogenesis-related genes. Front Immunol 2024; 15: 1239496.","journal-title":"Front Immunol"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"897","DOI":"10.2147\/JIR.S341871","article-title":"The specific changes of urine raman spectra can serve as novel diagnostic tools for disease characteristics in patients with Crohn’s disease","volume":"15","author":"Wu","year":"2022","unstructured":"Wu Y, Wang Z, Xing M, Li B, Liu Z, Du P, Yang H and Wang X. The specific changes of urine raman spectra can serve as novel diagnostic tools for disease characteristics in patients with Crohn’s disease. J Inflamm Res 2022; 15: 897-910.","journal-title":"J Inflamm Res"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"633","DOI":"10.3390\/jcm10040633","article-title":"Development of a clinical and genetic prediction model for early intestinal resection in patients with Crohn’s disease: results from the IMPACT study","volume":"10","author":"Kang","year":"2021","unstructured":"Kang EA, Jang J, Choi CH, Kang SB, Bang KB, Kim TO, Seo GS, Cha JM, Chun J, Jung Y, Kim HG, Im JP, Kim S, Ahn KS, Lee CK, Kim HJ, Kim MS and Park DI. Development of a clinical and genetic prediction model for early intestinal resection in patients with Crohn’s disease: results from the IMPACT study. J Clin Med 2021; 10: 633.","journal-title":"J Clin Med"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1093\/ibd\/izab035","article-title":"The use of readily available longitudinal data to predict the likelihood of surgery in crohn disease","volume":"27","author":"Stidham","year":"2021","unstructured":"Stidham RW, Liu Y, Enchakalody B, Van T, Krishnamurthy V, Su GL, Zhu J and Waljee AK. The use of readily available longitudinal data to predict the likelihood of surgery in crohn disease. Inflamm Bowel Dis 2021; 27: 1328-1334.","journal-title":"Inflamm Bowel Dis"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/s41467-018-07841-3","article-title":"Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response","volume":"10","author":"Haberman","year":"2019","unstructured":"Haberman Y, Karns R, Dexheimer PJ, Schirmer M, Somekh J, Jurickova I, Braun T, Novak E, Bauman L, Collins MH, Mo A, Rosen MJ, Bonkowski E, Gotman N, Marquis A, Nistel M, Rufo PA, Baker SS, Sauer CG, Markowitz J, Pfefferkorn MD, Rosh JR, Boyle BM, Mack DR, Baldassano RN, Shah S, Leleiko NS, Heyman MB, Grifiths AM, Patel AS, Noe JD, Aronow BJ, Kugathasan S, Walters TD, Gibson G, Thomas SD, Mollen K, Shen-Orr S, Huttenhower C, Xavier RJ, Hyams JS and Denson LA. Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. Nat Commun 2019; 10: 38.","journal-title":"Nat Commun"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1002\/jgh3.12342","article-title":"Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence","volume":"4","author":"Ghoshal","year":"2020","unstructured":"Ghoshal UC, Rai S, Kulkarni A and Gupta A. Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence. JGH Open 2020; 4: 889-897.","journal-title":"JGH Open"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1093\/gastro\/goaa070","article-title":"Multi-alleles predict primary non-response to infliximab therapy in Crohn’s disease","volume":"9","author":"Zhang","year":"2021","unstructured":"Zhang CB, Tang J, Wang XD, Lyu KS, Huang M and Gao X. Multi-alleles predict primary non-response to infliximab therapy in Crohn’s disease. Gastroenterol Rep (Oxf) 2021; 9: 427-434.","journal-title":"Gastroenterol Rep (Oxf)"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"947","DOI":"10.3390\/jpm12060947","article-title":"Development of a machine learning model to predict non-durable response to anti-TNF therapy in Crohn’s disease using transcriptome imputed from genotypes","volume":"12","author":"Park","year":"2022","unstructured":"Park SK, Kim YB, Kim S, Lee CW, Choi CH, Kang SB, Kim TO, Bang KB, Chun J, Cha JM, Im JP, Kim MS, Ahn KS, Kim SY and Park DI. Development of a machine learning model to predict non-durable response to anti-TNF therapy in Crohn’s disease using transcriptome imputed from genotypes. J Pers Med 2022; 12: 947.","journal-title":"J Pers Med"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1016\/j.cgh.2018.08.068","article-title":"Colonic MicroRNA profiles, identified by a deep learning algorithm, that predict responses to therapy of patients with acute severe ulcerative colitis","volume":"17","author":"Morilla","year":"2019","unstructured":"Morilla I, Uzzan M, Laharie D, Cazals-Hatem D, Denost Q, Daniel F, Belleannee G, Bouhnik Y, Wainrib G, Panis Y, Ogier-Denis E and Treton X. Colonic MicroRNA profiles, identified by a deep learning algorithm, that predict responses to therapy of patients with acute severe ulcerative colitis. Clin Gastroenterol Hepatol 2019; 17: 905-913.","journal-title":"Clin Gastroenterol Hepatol"},{"key":"ref33","doi-asserted-by":"publisher","unstructured":"Zhang T, Song B, Zhu W, Xu X, Gong QQ, Morando C, Dassopoulos T, Newberry RD, Hunt SR and Li E. An ileal Crohn’s disease gene signature based on whole human genome expression profiles of disease unaffected ileal mucosal biopsies. PLoS One 2012; 7: e37139.","DOI":"10.1371\/journal.pone.0037139"},{"key":"ref34","doi-asserted-by":"publisher","unstructured":"Duttagupta R, DiRienzo S, Jiang R, Bowers J, Gollub J, Kao J, Kearney K, Rudolph D, Dawany NB, Showe MK, Stamato T, Getts RC and Jones KW. Genome-wide maps of circulating miRNA biomarkers for ulcerative colitis. PLoS One 2012; 7: e31241.","DOI":"10.1371\/journal.pone.0031241"},{"key":"ref35","doi-asserted-by":"publisher","unstructured":"Hübenthal M, Hemmrich-Stanisak G, Degenhardt F, Szymczak S, Du Z, Elsharawy A, Keller A, Schreiber S and Franke A. Sparse modeling reveals miRNA signatures for diagnostics of inflammatory bowel disease. PLoS One 2015; 10: e0140155.","DOI":"10.1371\/journal.pone.0140155"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13073-015-0162-2","article-title":"Transcriptomic landscape of lncRNAs in inflammatory bowel disease","volume":"7","author":"Mirza","year":"2015","unstructured":"Mirza AH, Berthelsen CH, Seemann SE, Pan X, Frederiksen KS, Vilien M, Gorodkin J and Pociot F. Transcriptomic landscape of lncRNAs in inflammatory bowel disease. Genome Med 2015; 7: 39.","journal-title":"Genome Med"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1038\/s41598-017-02606-2","article-title":"Classification of paediatric inflammatory bowel disease using machine learning","volume":"7","author":"Mossotto","year":"2017","unstructured":"Mossotto E, Ashton JJ, Coelho T, Beattie RM, MacArthur BD and Ennis S. Classification of paediatric inflammatory bowel disease using machine learning. Sci Rep 2017; 7: 2427.","journal-title":"Sci Rep"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"4510","DOI":"10.3748\/wjg.v24.i39.4510","article-title":"Fungal dysbiosis predicts the diagnosis of pediatric Crohn’s disease","volume":"24","author":"El Mouzan","year":"2018","unstructured":"El Mouzan MI, Korolev KS, Al Mofarreh MA, Menon R, Winter HS, Al Sarkhy AA, Dowd SE, Al Barrag AM and Assiri AA. Fungal dysbiosis predicts the diagnosis of pediatric Crohn’s disease. World J Gastroenterol 2018; 24: 4510-4516.","journal-title":"World J Gastroenterol"},{"key":"ref39","doi-asserted-by":"publisher","unstructured":"Klein O, Fogt F, Hollerbach S, Nebrich G, Boskamp T and Wellmann A. Classification of inflammatory bowel disease from Formalin-Fixed, Paraffin-Embedded tissue biopsies via imaging mass spectrometry. Proteomics Clin Appl 2020; 14: e1900131.","DOI":"10.1002\/prca.201900131"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/jgh.15232","article-title":"Gut microbiome diversity in acute severe colitis is distinct from mild to moderate ulcerative colitis","volume":"36","author":"Kedia","year":"2021","unstructured":"Kedia S, Ghosh TS, Jain S, Desigamani A, Kumar A, Gupta V, Bopanna S, Yadav DP, Goyal S, Makharia G, Travis SPL, Das B and Ahuja V. Gut microbiome diversity in acute severe colitis is distinct from mild to moderate ulcerative colitis. J Gastroenterol Hepatol 2021; 36: 731-739.","journal-title":"J Gastroenterol Hepatol"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"13744","DOI":"10.1038\/s41598-020-70583-0","article-title":"Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning","volume":"10","author":"HM","year":"2020","unstructured":"Khorasani HM, Usefi H and Peña-Castillo L. Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning. Sci Rep 2020; 10: 13744.","journal-title":"Sci Rep"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"20471","DOI":"10.18632\/aging.103861","article-title":"Development of a susceptibility gene based novel predictive model for the diagnosis of ulcerative colitis using random forest and artificial neural network","volume":"12","author":"Li","year":"2020","unstructured":"Li H, Lai L and Shen J. Development of a susceptibility gene based novel predictive model for the diagnosis of ulcerative colitis using random forest and artificial neural network. Aging (Albany NY) 2020; 12: 20471-20482.","journal-title":"Aging (Albany NY)"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1590\/s0004-2803.202000000-57","article-title":"Use of metabolomics to the diagnosis of inflammatory bowel disease","volume":"57","author":"Neto M&#xC1","year":"2020","unstructured":"Lins Neto MÁF, Verdi GMX, Veras AO, Veras MO, Caetano LC and Ursulino JS. Use of metabolomics to the diagnosis of inflammatory bowel disease. Arq Gastroenterol 2020; 57: 311-315.","journal-title":"Arq Gastroenterol"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13040-021-00241-2","article-title":"LightCUD: a program for diagnosing IBD based on human gut microbiome data","volume":"14","author":"Xu","year":"2021","unstructured":"Xu C, Zhou M, Xie Z, Li M, Zhu X and Zhu H. LightCUD: a program for diagnosing IBD based on human gut microbiome data. BioData Min 2021; 14: 2.","journal-title":"BioData Min"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1097\/MPG.0000000000002956","article-title":"Accurate classification of pediatric colonic inflammatory bowel disease subtype using a random forest machine learning classifier","volume":"72","author":"Dhaliwal","year":"2021","unstructured":"Dhaliwal J, Erdman L, Drysdale E, Rinawi F, Muir J, Walters TD, Siddiqui I, Griffiths AM and Church PC. Accurate classification of pediatric colonic inflammatory bowel disease subtype using a random forest machine learning classifier. J Pediatr Gastroenterol Nutr 2021; 72: 262-269.","journal-title":"J Pediatr Gastroenterol Nutr"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"144","DOI":"10.4103\/sjg.SJG_409_20","article-title":"Bacterial dysbiosis predicts the diagnosis of Crohn’s disease in Saudi children","volume":"27","author":"El Mouzan","year":"2021","unstructured":"El Mouzan MI, Winter HS, Al Sarkhy AA, Korolev K, Menon R and Assiri AA. Bacterial dysbiosis predicts the diagnosis of Crohn’s disease in Saudi children. Saudi J Gastroenterol 2021; 27: 144-148.","journal-title":"Saudi J Gastroenterol"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"603740","DOI":"10.3389\/fmolb.2020.603740","article-title":"Metabolic phenotypes as potential biomarkers for linking gut microbiome with inflammatory bowel diseases","volume":"7","author":"Iablokov","year":"2020","unstructured":"Iablokov SN, Klimenko NS, Efimova DA, Shashkova T, Novichkov PS, Rodionov DA and Tyakht AV. Metabolic phenotypes as potential biomarkers for linking gut microbiome with inflammatory bowel diseases. Front Mol Biosci 2020; 7: 603740.","journal-title":"Front Mol Biosci"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"4745","DOI":"10.3390\/jcm10204745","article-title":"Working model in a discovery cohort study","volume":"10","author":"Kraszewski","year":"2021","unstructured":"Kraszewski S, Szczurek W, Szymczak J, Reguła M and Neubauer K. Machine learning prediction model for inflammatory bowel disease based on laboratory markers. Working model in a discovery cohort study. J Clin Med 2021; 10: 4745.","journal-title":"J Clin Med"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"5264","DOI":"10.1039\/D1AY01377G","article-title":"Non-invasive diagnosis of Crohn’s disease based on SERS combined with PCA-SVM","volume":"13","author":"Li","year":"2021","unstructured":"Li B, Wu Y, Wang Z, Xing M, Xu W, Zhu Y, Du P, Wang X and Yang H. Non-invasive diagnosis of Crohn’s disease based on SERS combined with PCA-SVM. Anal Methods 2021; 13: 5264-5273.","journal-title":"Anal Methods"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s13577-021-00641-w","article-title":"Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning","volume":"35","author":"Lu","year":"2022","unstructured":"Lu J, Wang Z, Maimaiti M, Hui W, Abudourexiti A and Gao F. Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning. Hum Cell 2022; 35: 179-188.","journal-title":"Hum Cell"},{"key":"ref51","doi-asserted-by":"publisher","unstructured":"Manandhar I, Alimadadi A, Aryal S, Munroe PB, Joe B and Cheng X. Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. Am J Physiol Gastrointest Liver Physiol 2021; 320: G328-G337.","DOI":"10.1152\/ajpgi.00360.2020"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1007\/s00109-021-02094-y","article-title":"Targeted (1)H NMR metabolomics and immunological phenotyping of human fresh blood and serum samples discriminate between healthy individuals and inflammatory bowel disease patients treated with anti-TNF","volume":"99","author":"Notararigo","year":"2021","unstructured":"Notararigo S, Martín-Pastor M, Viñuela-Roldán JE, Quiroga A, Dominguez-Munoz JE and Barreiro-de Acosta M. Targeted (1)H NMR metabolomics and immunological phenotyping of human fresh blood and serum samples discriminate between healthy individuals and inflammatory bowel disease patients treated with anti-TNF. J Mol Med (Berl) 2021; 99: 1251-1264.","journal-title":"J Mol Med (Berl)"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.3390\/diagnostics11122365","article-title":"Development of a machine learning model to distinguish between ulcerative colitis and Crohn’s disease using RNA sequencing data","volume":"11","author":"Park","year":"2021","unstructured":"Park SK, Kim S, Lee GY, Kim SY, Kim W, Lee CW, Park JL, Choi CH, Kang SB, Kim TO, Bang KB, Chun J, Cha JM, Im JP, Ahn KS, Kim SY and Park DI. Development of a machine learning model to distinguish between ulcerative colitis and Crohn’s disease using RNA sequencing data. Diagnostics (Basel) 2021; 11: 2365.","journal-title":"Diagnostics (Basel)"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"666190","DOI":"10.3389\/fmed.2021.666190","article-title":"Machine learning classification of inflammatory bowel disease in children based on a large real-world pediatric cohort CEDATA-GPGE® registry","volume":"8","author":"Schneider","year":"2021","unstructured":"Schneider N, Sohrabi K, Schneider H, Zimmer KP, Fischer P and de Laffolie J; CEDATA-GPGE Study Group. Machine learning classification of inflammatory bowel disease in children based on a large real-world pediatric cohort CEDATA-GPGE® registry. Front Med (Lausanne) 2021; 8: 666190.","journal-title":"Front Med (Lausanne)"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.3390\/healthcare10081476","article-title":"Artificial intelligence analysis of ulcerative colitis using an autoimmune discovery transcriptomic panel","volume":"10","author":"Carreras","year":"2022","unstructured":"Carreras J. Artificial intelligence analysis of ulcerative colitis using an autoimmune discovery transcriptomic panel. Healthcare (Basel) 2022; 10: 1476.","journal-title":"Healthcare (Basel)"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.3390\/genes13091570","article-title":"Unexpected actors in inflammatory bowel disease revealed by machine learning from whole-blood transcriptomic data","volume":"13","author":"JK","year":"2022","unstructured":"Nowak JK, Szymańska CJ, Glapa-Nowak A, Duclaux-Loras R, Dybska E, Ostrowski J, Walkowiak J and Adams AT. Unexpected actors in inflammatory bowel disease revealed by machine learning from whole-blood transcriptomic data. Genes (Basel) 2022; 13: 1570.","journal-title":"Genes (Basel)"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"976578","DOI":"10.3389\/fgene.2022.976578","article-title":"Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease","volume":"13","author":"Yang","year":"2022","unstructured":"Yang Y, Xu L, Qiao Y, Wang T and Zheng Q. Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn’s disease. Front Genet 2022; 13: 976578.","journal-title":"Front Genet"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"6818","DOI":"10.1038\/s41467-022-34405-3","article-title":"Faecal microbiome-based machine learning for multi-class disease diagnosis","volume":"13","author":"Su","year":"2022","unstructured":"Su Q, Liu Q, Lau RI, Zhang J, Xu Z, Yeoh YK, Leung TWH, Tang W, Zhang L, Liang JQY, Yau YK, Zheng J, Liu C, Zhang M, Cheung CP, Ching JYL, Tun HM, Yu J, Chan FKL and Ng SC. Faecal microbiome-based machine learning for multi-class disease diagnosis. Nat Commun 2022; 13: 6818.","journal-title":"Nat Commun"},{"key":"ref59","doi-asserted-by":"publisher","first-page":"5412627","DOI":"10.1155\/2022\/5412627","article-title":"Identification of potential biomarkers and immune infiltration characteristics in ulcerative colitis by combining results from two machine learning algorithms","volume":"2022","author":"Bu","year":"2022","unstructured":"Bu M, Cao X and Zhou B. Identification of potential biomarkers and immune infiltration characteristics in ulcerative colitis by combining results from two machine learning algorithms. Comput Math Methods Med 2022; 2022: 5412627.","journal-title":"Comput Math Methods Med"},{"key":"ref60","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1093\/ibd\/izab256","article-title":"Fecal Amino Acid analysis in newly diagnosed pediatric inflammatory bowel disease: a multicenter case-control study","volume":"28","author":"Jagt","year":"2022","unstructured":"Jagt JZ, Struys EA, Ayada I, Bakkali A, Jansen EEW, Claesen J, van Limbergen JE, Benninga MA, de Boer NKH and de Meij TGJ. Fecal Amino Acid analysis in newly diagnosed pediatric inflammatory bowel disease: a multicenter case-control study. Inflamm Bowel Dis 2022; 28: 755-763.","journal-title":"Inflamm Bowel Dis"},{"key":"ref61","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/microorganisms12010036","article-title":"A machine learning-based diagnostic model for Crohn’s disease and ulcerative colitis utilizing fecal microbiome analysis","volume":"12","author":"Kim","year":"2023","unstructured":"Kim H, Na JE, Kim S, Kim TO, Park SK, Lee CW, Kim KO, Seo GS, Kim MS, Cha JM, Koo JS and Park DI. A machine learning-based diagnostic model for Crohn’s disease and ulcerative colitis utilizing fecal microbiome analysis. Microorganisms 2023; 12: 36.","journal-title":"Microorganisms"},{"key":"ref62","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1186\/s12866-023-03084-5","article-title":"Diagnosis of Crohn’s disease and ulcerative colitis using the microbiome","volume":"23","author":"Kang","year":"2023","unstructured":"Kang DY, Park JL, Yeo MK, Kang SB, Kim JM, Kim JS and Kim SY. Diagnosis of Crohn’s disease and ulcerative colitis using the microbiome. BMC Microbiol 2023; 23: 336.","journal-title":"BMC Microbiol"},{"key":"ref63","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.3390\/medicina59071218","article-title":"Gene identification in inflammatory bowel disease via a machine learning approach","volume":"59","author":"Alfonso Perez","year":"2023","unstructured":"Alfonso Perez G and Castillo R. Gene identification in inflammatory bowel disease via a machine learning approach. Medicina (Kaunas) 2023; 59: 1218.","journal-title":"Medicina (Kaunas)"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"5255","DOI":"10.1007\/s10238-023-01145-7","article-title":"Identification of antigen-presentation related B cells as a key player in Crohn’s disease using single-cell dissecting, hdWGCNA, and deep learning","volume":"23","author":"Shen","year":"2023","unstructured":"Shen X, Mo S, Zeng X, Wang Y, Lin L, Weng M, Sugasawa T, Wang L, Gu W and Nakajima T. Identification of antigen-presentation related B cells as a key player in Crohn’s disease using single-cell dissecting, hdWGCNA, and deep learning. Clin Exp Med 2023; 23: 5255-5267.","journal-title":"Clin Exp Med"},{"key":"ref65","doi-asserted-by":"publisher","first-page":"177","DOI":"10.21037\/atm-23-276","article-title":"Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning","volume":"11","author":"Qian","year":"2023","unstructured":"Qian R, Tang M, Ouyang Z, Cheng H and Xing S. Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning. Ann Transl Med 2023; 11: 177.","journal-title":"Ann Transl Med"},{"key":"ref66","doi-asserted-by":"publisher","first-page":"4373840","DOI":"10.1155\/2023\/4373840","article-title":"Identifying macrophage-related genes in ulcerative colitis using weighted coexpression network analysis and machine learning","volume":"2023","author":"Hong","year":"2023","unstructured":"Hong S, Wang H, Chan S, Zhang J, Chen B, Ma X and Chen X. Identifying macrophage-related genes in ulcerative colitis using weighted coexpression network analysis and machine learning. Mediators Inflamm 2023; 2023: 4373840.","journal-title":"Mediators Inflamm"},{"key":"ref67","doi-asserted-by":"publisher","first-page":"156","DOI":"10.12688\/f1000research.108810.1","article-title":"Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America","volume":"11","author":"Shah","year":"2022","unstructured":"Shah R, Hoffman K, Denson L, Kugathasan S and Kellermayer R. Mucosal microbiome is predictive of pediatric Crohn’s disease across geographic regions in North America. F1000Res 2022; 11: 156.","journal-title":"F1000Res"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"991247","DOI":"10.3389\/fped.2023.991247","article-title":"Pediatric Crohn’s disease diagnosis aid via genomic analysis and machine learning","volume":"11","author":"Zheng","year":"2023","unstructured":"Zheng Z, Zhan S, Zhou Y, Huang G, Chen P and Li B. Pediatric Crohn’s disease diagnosis aid via genomic analysis and machine learning. Front Pediatr 2023; 11: 991247.","journal-title":"Front Pediatr"},{"key":"ref69","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.3390\/microorganisms11071665","article-title":"Potential oral microbial markers for differential diagnosis of Crohn’s disease and ulcerative colitis using machine learning models","volume":"11","author":"Kang","year":"2023","unstructured":"Kang SB, Kim H, Kim S, Kim J, Park SK, Lee CW, Kim KO, Seo GS, Kim MS, Cha JM, Koo JS and Park DI. Potential oral microbial markers for differential diagnosis of Crohn’s disease and ulcerative colitis using machine learning models. Microorganisms 2023; 11: 1665.","journal-title":"Microorganisms"},{"key":"ref70","doi-asserted-by":"publisher","first-page":"20220731","DOI":"10.1515\/biol-2022-0731","article-title":"Predicting pediatric Crohn’s disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches","volume":"18","author":"Zhan","year":"2023","unstructured":"Zhan Y, Jin Q, Yousif TYE, Soni M, Ren Y and Liu S. Predicting pediatric Crohn’s disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches. Open Life Sci 2023; 18: 20220731.","journal-title":"Open Life Sci"},{"key":"ref71","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.3724\/abbs.2023237","article-title":"Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis","volume":"55","author":"Mo","year":"2023","unstructured":"Mo S, Shen X, Huang B, Wang Y, Lin L, Chen Q, Weng M, Sugasawa T, Gu W, Tsushima Y and Nakajima T. Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis. Acta Biochim Biophys Sin (Shanghai) 2023; 55: 1730-1739.","journal-title":"Acta Biochim Biophys Sin (Shanghai)"},{"key":"ref72","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1093\/ecco-jcc\/jjad084","article-title":"Supervised machine learning classifies inflammatory bowel disease patients by subtype using whole exome sequencing data","volume":"17","author":"Stafford","year":"2023","unstructured":"Stafford IS, Ashton JJ, Mossotto E, Cheng G, Mark Beattie R and Ennis S. Supervised machine learning classifies inflammatory bowel disease patients by subtype using whole exome sequencing data. J Crohns Colitis 2023; 17: 1672-1680.","journal-title":"J Crohns Colitis"},{"key":"ref73","doi-asserted-by":"publisher","first-page":"2292254","DOI":"10.1080\/19490976.2023.2292254","article-title":"The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning","volume":"16","author":"Wu","year":"2024","unstructured":"Wu X, Zhang T, Zhang T and Park S. The impact of gut microbiome enterotypes on ulcerative colitis: identifying key bacterial species and revealing species co-occurrence networks using machine learning. Gut Microbes 2024; 16: 2292254.","journal-title":"Gut Microbes"},{"key":"ref74","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.1038\/s41598-022-26345-1","article-title":"Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn’s disease based on three machine learning methods","volume":"13","author":"Chen","year":"2023","unstructured":"Chen ZA, Ma HH, Wang Y, Tian H, Mi JW, Yao DM and Yang CJ. Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn’s disease based on three machine learning methods. Sci Rep 2023; 13: 2694.","journal-title":"Sci Rep"},{"key":"ref75","doi-asserted-by":"publisher","first-page":"3856","DOI":"10.18632\/aging.205564","article-title":"An assessment system for clinical and biological interpretability in ulcerative colitis","volume":"16","author":"Zhang","year":"2024","unstructured":"Zhang S, Zhang G, Wang W, Guo SB, Zhang P, Wang F, Zhou Q, Zhou Z, Wang Y, Sun H, Cui W, Yang S and Yuan W. An assessment system for clinical and biological interpretability in ulcerative colitis. Aging (Albany NY) 2024; 16: 3856-3879.","journal-title":"Aging (Albany NY)"},{"key":"ref76","doi-asserted-by":"publisher","first-page":"2491","DOI":"10.3390\/diagnostics13152491","article-title":"Machine learning in antibody diagnostics for inflammatory bowel disease subtype classification","volume":"13","author":"Sokollik","year":"2023","unstructured":"Sokollik C, Pahud de Mortanges A, Leichtle AB, Juillerat P and Horn MP; Swiss IBD Cohort Study Group. Machine learning in antibody diagnostics for inflammatory bowel disease subtype classification. Diagnostics (Basel) 2023; 13: 2491.","journal-title":"Diagnostics (Basel)"},{"key":"ref77","doi-asserted-by":"publisher","first-page":"2221428","DOI":"10.1080\/19490976.2023.2221428","article-title":"Microbial genes outperform species and SNVs as diagnostic markers for Crohn’s disease on multicohort fecal metagenomes empowered by artificial intelligence","volume":"15","author":"Gao","year":"2023","unstructured":"Gao S, Gao X, Zhu R, Wu D, Feng Z, Jiao N, Sun R, Gao W, He Q, Liu Z and Zhu L. Microbial genes outperform species and SNVs as diagnostic markers for Crohn’s disease on multicohort fecal metagenomes empowered by artificial intelligence. Gut Microbes 2023; 15: 2221428.","journal-title":"Gut Microbes"},{"key":"ref78","doi-asserted-by":"publisher","first-page":"107778","DOI":"10.1016\/j.compbiomed.2023.107778","article-title":"Biomarkers prediction and immune landscape in ulcerative colitis: findings based on bioinformatics and machine learning","volume":"168","author":"Yang","year":"2024","unstructured":"Yang Y, Hua Y, Zheng H, Jia R, Ye Z, Su G, Gu Y, Zhan K, Tang K, Qi S, Wu H, Qin S and Huang S. Biomarkers prediction and immune landscape in ulcerative colitis: findings based on bioinformatics and machine learning. Comput Biol Med 2024; 168: 107778.","journal-title":"Comput Biol Med"},{"key":"ref79","doi-asserted-by":"publisher","first-page":"1323418","DOI":"10.3389\/fimmu.2024.1323418","article-title":"Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning","volume":"15","author":"Tang","year":"2024","unstructured":"Tang D, Huang Y, Che Y, Yang C, Pu B, Liu S and Li H. Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn’s disease based on WGCNA and machine learning. Front Immunol 2024; 15: 1323418.","journal-title":"Front Immunol"},{"key":"ref80","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1038\/s41467-024-45855-2","article-title":"Mucosal host-microbe interactions associate with clinical phenotypes in inflammatory bowel disease","volume":"15","author":"Hu","year":"2024","unstructured":"Hu S, Bourgonje AR, Gacesa R, Jansen BH, Björk JR, Bangma A, Hidding IJ, van Dullemen HM, Visschedijk MC, Faber KN, Dijkstra G, Harmsen HJM, Festen EAM, Vich Vila A, Spekhorst LM and Weersma RK. Mucosal host-microbe interactions associate with clinical phenotypes in inflammatory bowel disease. Nat Commun 2024; 15: 1470.","journal-title":"Nat Commun"},{"key":"ref81","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1002\/2211-5463.13357","article-title":"Integrative computational approach identifies immune-relevant biomarkers in ulcerative colitis","volume":"12","author":"He","year":"2022","unstructured":"He T, Wang K, Zhao P, Zhu G, Yin X, Zhang Y, Zhang Z, Zhao K, Wang Z and Wang K. Integrative computational approach identifies immune-relevant biomarkers in ulcerative colitis. FEBS Open Bio 2022; 12: 500-515.","journal-title":"FEBS Open Bio"},{"key":"ref82","doi-asserted-by":"publisher","first-page":"9962","DOI":"10.1038\/s41598-022-14048-6","article-title":"Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods","volume":"12","author":"Zhang","year":"2022","unstructured":"Zhang L, Mao R, Lau CT, Chung WC, Chan JCP, Liang F, Zhao C, Zhang X and Bian Z. Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods. Sci Rep 2022; 12: 9962.","journal-title":"Sci Rep"},{"key":"ref83","doi-asserted-by":"publisher","first-page":"1142215","DOI":"10.3389\/fimmu.2023.1142215","article-title":"Identification of cuproptosis-associated subtypes and signature genes for diagnosis and risk prediction of Ulcerative colitis based on machine learning","volume":"14","author":"Tang","year":"2023","unstructured":"Tang D, Pu B, Liu S and Li H. Identification of cuproptosis-associated subtypes and signature genes for diagnosis and risk prediction of Ulcerative colitis based on machine learning. Front Immunol 2023; 14: 1142215.","journal-title":"Front Immunol"},{"key":"ref84","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s41065-022-00259-4","article-title":"Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis","volume":"159","author":"Li","year":"2022","unstructured":"Li Y, Tang M, Zhang FJ, Huang Y, Zhang J, Li J, Wang Y, Yang J and Zhu S. Screening of ulcerative colitis biomarkers and potential pathways based on weighted gene co-expression network, machine learning and ceRNA hypothesis. Hereditas 2022; 159: 42.","journal-title":"Hereditas"},{"key":"ref85","doi-asserted-by":"publisher","first-page":"6081","DOI":"10.1038\/s41598-023-33388-5","article-title":"Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis","volume":"13","author":"Chen","year":"2023","unstructured":"Chen Q, Bei S, Zhang Z, Wang X and Zhu Y. Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis. Sci Rep 2023; 13: 6081.","journal-title":"Sci Rep"},{"key":"ref86","doi-asserted-by":"publisher","unstructured":"Wu GL, Li L, Chen XY, Zhang WF, Wu JB, Yu X and Chen HJ. Machine learning-based B cell-related diagnostic biomarker signature and molecular subtypes characteristic of ulcerative colitis. Aging (Albany NY) 2024; 16: 2774-2788.","DOI":"10.18632\/aging.205510"},{"key":"ref87","doi-asserted-by":"publisher","unstructured":"Waljee AK, Cohen-Mekelburg S, Liu Y, Liu B, Zhu J and Higgins PDR. Assessing clinical disease recurrence using laboratory data in surgically resected patients from the TOPPIC trial. Crohns Colitis 360 2020; 2: otaa088.","DOI":"10.1093\/crocol\/otaa088"},{"key":"ref88","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1055\/a-0981-6516","article-title":"Genetic prediction profile for adalimumab response in Slovenian Crohn’s disease patients","volume":"57","author":"Gorenjak","year":"2019","unstructured":"Gorenjak M, Repnik K, Jezernik G, Jurgec S, Skok P and Potočnik U. Genetic prediction profile for adalimumab response in Slovenian Crohn’s disease patients. Z Gastroenterol 2019; 57: 1218-1225.","journal-title":"Z Gastroenterol"},{"key":"ref89","doi-asserted-by":"publisher","first-page":"19186","DOI":"10.1038\/s41598-020-76175-2","article-title":"Mucosal microbiota and gene expression are associated with long-term remission after discontinuation of adalimumab in ulcerative colitis","volume":"10","author":"Sakurai","year":"2020","unstructured":"Sakurai T, Nishiyama H, Sakai K, De Velasco MA, Nagai T, Komeda Y, Kashida H, Okada A, Kawai I, Nishio K, Ogata H and Kudo M. Mucosal microbiota and gene expression are associated with long-term remission after discontinuation of adalimumab in ulcerative colitis. Sci Rep 2020; 10: 19186.","journal-title":"Sci Rep"},{"key":"ref90","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/metabo14010002","article-title":"Prediction of clinical remission with adalimumab therapy in patients with ulcerative colitis by fourier transform-infrared spectroscopy coupled with machine learning algorithms","volume":"14","author":"Kim","year":"2023","unstructured":"Kim SY, Shin SY, Saeed M, Ryu JE, Kim JS, Ahn J, Jung Y, Moon JM, Choi CH and Choi HK. Prediction of clinical remission with adalimumab therapy in patients with ulcerative colitis by fourier transform-infrared spectroscopy coupled with machine learning algorithms. Metabolites 2023; 14: 2.","journal-title":"Metabolites"},{"key":"ref91","doi-asserted-by":"publisher","first-page":"917","DOI":"10.2147\/PPA.S253732","article-title":"Applying machine learning models to predict medication nonadherence in Crohn’s disease maintenance therapy","volume":"14","author":"Wang","year":"2020","unstructured":"Wang L, Fan R, Zhang C, Hong L, Zhang T, Chen Y, Liu K, Wang Z and Zhong J. Applying machine learning models to predict medication nonadherence in Crohn’s disease maintenance therapy. Patient Prefer Adherence 2020; 14: 917-926.","journal-title":"Patient Prefer Adherence"},{"key":"ref92","doi-asserted-by":"publisher","unstructured":"Yu S, Li H, Li Y, Xu H, Tan B, Tian BW, Dai YM, Tian F and Qian JM. Development and validation of novel models for the prediction of intravenous corticosteroid resistance in acute severe ulcerative colitis using logistic regression and machine learning. Gastroenterol Rep (Oxf) 2022; 10: goac053.","DOI":"10.1093\/gastro\/goac053"},{"key":"ref93","doi-asserted-by":"publisher","unstructured":"Takayama T, Okamoto S, Hisamatsu T, Naganuma M, Matsuoka K, Mizuno S, Bessho R, Hibi T and Kanai T. Computer-aided prediction of long-term prognosis of patients with ulcerative colitis after cytoapheresis therapy. PLoS One 2015; 10: e0131197.","DOI":"10.1371\/journal.pone.0131197"},{"key":"ref94","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1093\/ibd\/izaa001","article-title":"Bacterial taxa and functions are predictive of sustained remission following exclusive enteral nutrition in pediatric Crohn’s disease","volume":"26","author":"Jones","year":"2020","unstructured":"Jones CMA, Connors J, Dunn KA, Bielawski JP, Comeau AM, Langille MGI and Van Limbergen J. Bacterial taxa and functions are predictive of sustained remission following exclusive enteral nutrition in pediatric Crohn’s disease. Inflamm Bowel Dis 2020; 26: 1026-1037.","journal-title":"Inflamm Bowel Dis"},{"key":"ref95","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1002\/cpt.3076","article-title":"Machine learning-based quantification of patient factors impacting remission in patients with ulcerative colitis: insights from etrolizumab phase III clinical trials","volume":"115","author":"Harun","year":"2024","unstructured":"Harun R, Lu J, Kassir N and Zhang W. Machine learning-based quantification of patient factors impacting remission in patients with ulcerative colitis: insights from etrolizumab phase III clinical trials. Clin Pharmacol Ther 2024; 115: 815-824.","journal-title":"Clin Pharmacol Ther"},{"key":"ref96","doi-asserted-by":"publisher","first-page":"775","DOI":"10.5009\/gnl210369","article-title":"Exploration of potential gut microbiota-derived biomarkers to predict the success of fecal microbiota transplantation in ulcerative colitis: a prospective cohort in Korea","volume":"16","author":"Kang","year":"2022","unstructured":"Kang GU, Park S, Jung Y, Jee JJ, Kim MS, Lee S, Lee DW, Shin JH and Koh H. Exploration of potential gut microbiota-derived biomarkers to predict the success of fecal microbiota transplantation in ulcerative colitis: a prospective cohort in Korea. Gut Liver 2022; 16: 775-785.","journal-title":"Gut Liver"},{"key":"ref97","doi-asserted-by":"publisher","first-page":"3340","DOI":"10.3390\/nu15153340","article-title":"The underlying changes in serum metabolic profiles and efficacy prediction in patients with extensive ulcerative colitis undergoing fecal microbiota transplantation","volume":"15","author":"Wu","year":"2023","unstructured":"Wu X, Li P, Wang W, Xu J, Ai R, Wen Q, Cui B and Zhang F. The underlying changes in serum metabolic profiles and efficacy prediction in patients with extensive ulcerative colitis undergoing fecal microbiota transplantation. Nutrients 2023; 15: 3340.","journal-title":"Nutrients"},{"key":"ref98","doi-asserted-by":"publisher","unstructured":"Telesco SE, Brodmerkel C, Zhang H, Kim LL, Johanns J, Mazumder A, Li K, Baribaud F, Curran M, Strauss R, Paxson B, Plevy S, Davison T, Knight L, Dibben S, Schreiber S, Sandborn W, Rutgeerts P, Siegel CA, Reinisch W and Greenbaum LE. Gene expression signature for prediction of golimumab response in a phase 2a open-label trial of patients with ulcerative colitis. Gastroenterology 2018; 155: 1008-1011, e1008.","DOI":"10.1053\/j.gastro.2018.06.077"},{"key":"ref99","doi-asserted-by":"publisher","first-page":"678424","DOI":"10.3389\/fmed.2021.678424","article-title":"Novel gene signatures predicting primary non-response to infliximab in ulcerative colitis: development and validation combining random forest with artificial neural network","volume":"8","author":"Feng","year":"2021","unstructured":"Feng J, Chen Y, Feng Q, Ran Z and Shen J. Novel gene signatures predicting primary non-response to infliximab in ulcerative colitis: development and validation combining random forest with artificial neural network. Front Med (Lausanne) 2021; 8: 678424.","journal-title":"Front Med (Lausanne)"},{"key":"ref100","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.trsl.2022.03.006","article-title":"Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis","volume":"246","author":"Ghiassian","year":"2022","unstructured":"Ghiassian SD, Voitalov I, Withers JB, Santolini M, Saleh A and Akmaev VR. Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis. Transl Res 2022; 246: 78-86.","journal-title":"Transl Res"},{"key":"ref101","doi-asserted-by":"publisher","first-page":"646673","DOI":"10.3389\/fimmu.2021.646673","article-title":"A Novel model based on serum biomarkers to predict primary non-response to infliximab in Crohn’s disease","volume":"12","author":"Li","year":"2021","unstructured":"Li L, Chen R, Zhang Y, Zhou G, Chen B, Zeng Z, Chen M and Zhang S. A Novel model based on serum biomarkers to predict primary non-response to infliximab in Crohn’s disease. Front Immunol 2021; 12: 646673.","journal-title":"Front Immunol"},{"key":"ref102","doi-asserted-by":"publisher","first-page":"742080","DOI":"10.3389\/fimmu.2021.742080","article-title":"Artificial neural network analysis-based immune-related signatures of primary non-response to infliximab in patients with ulcerative colitis","volume":"12","author":"Chen","year":"2021","unstructured":"Chen X, Jiang L, Han W, Bai X, Ruan G, Guo M, Zhou R, Liang H, Yang H and Qian J. Artificial neural network analysis-based immune-related signatures of primary non-response to infliximab in patients with ulcerative colitis. Front Immunol 2021; 12: 742080.","journal-title":"Front Immunol"},{"key":"ref103","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s13073-022-01112-z","article-title":"Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease","volume":"14","author":"Mishra","year":"2022","unstructured":"Mishra N, Aden K, Blase JI, Baran N, Bordoni D, Tran F, Conrad C, Avalos D, Jaeckel C, Scherer M, Sørensen SB, Overgaard SH, Schulte B, Nikolaus S, Rey G, Gasparoni G, Lyons PA, Schultze JL, Walter J, Andersen V; SYSCID Consortium; Dermitzakis ET, Schreiber S and Rosenstiel P. Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease. Genome Med 2022; 14: 110.","journal-title":"Genome Med"},{"key":"ref104","doi-asserted-by":"publisher","unstructured":"Derakhshan Nazari MH, Shahrokh S, Ghanbari-Maman L, Maleknia S, Ghorbaninejad M and Meyfour A. Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: a prospective study. Heliyon 2023; 9: e21154.","DOI":"10.1016\/j.heliyon.2023.e21154"},{"key":"ref105","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1080\/00365521.2021.1939411","article-title":"A random forest model predicts responses to infliximab in Crohn’s disease based on clinical and serological parameters","volume":"56","author":"Li","year":"2021","unstructured":"Li Y, Pan J, Zhou N, Fu D, Lian G, Yi J, Peng Y and Liu X. A random forest model predicts responses to infliximab in Crohn’s disease based on clinical and serological parameters. Scand J Gastroenterol 2021; 56: 1030-1039.","journal-title":"Scand J Gastroenterol"},{"key":"ref106","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1093\/ecco-jcc\/jjad213","article-title":"Baseline serum and stool microbiome biomarkers predict clinical efficacy and tissue molecular response after ritlecitinib induction therapy in ulcerative colitis","volume":"18","author":"Hassan-Zahraee","year":"2024","unstructured":"Hassan-Zahraee M, Ye Z, Xi L, Dushin E, Lee J, Romatowski J, Leszczyszyn J, Danese S, Sandborn WJ, Banfield C, Gale JD, Peeva E, Longman RS, Hyde CL and Hung KE. Baseline serum and stool microbiome biomarkers predict clinical efficacy and tissue molecular response after ritlecitinib induction therapy in ulcerative colitis. J Crohns Colitis 2024; 18: 1361-1370.","journal-title":"J Crohns Colitis"},{"key":"ref107","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1093\/ecco-jcc\/jjx014","article-title":"Machine learning algorithms for objective remission and clinical outcomes with thiopurines","volume":"11","author":"Waljee","year":"2017","unstructured":"Waljee AK, Sauder K, Patel A, Segar S, Liu B, Zhang Y, Zhu J, Stidham RW, Balis U and Higgins PDR. Machine learning algorithms for objective remission and clinical outcomes with thiopurines. J Crohns Colitis 2017; 11: 801-810.","journal-title":"J Crohns Colitis"},{"key":"ref108","doi-asserted-by":"publisher","first-page":"17562848211054710","DOI":"10.1177\/17562848211054710","article-title":"Prediction of early clinical response in patients receiving tofacitinib in the OCTAVE Induction 1 and 2 studies","volume":"14","author":"Lees","year":"2021","unstructured":"Lees CW, Deuring JJ, Chiorean M, Daperno M, Bonfanti G, Germino R, Brown PB, Modesto I and Edwards RA. Prediction of early clinical response in patients receiving tofacitinib in the OCTAVE Induction 1 and 2 studies. Therap Adv Gastroenterol 2021; 14: 17562848211054710.","journal-title":"Therap Adv Gastroenterol"},{"key":"ref109","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1093\/ecco-jcc\/jjad129","article-title":"Peripheral blood DNA methylation signatures and response to tofacitinib in moderate-to-severe ulcerative colitis","volume":"18","author":"Joustra","year":"2024","unstructured":"Joustra V, Li Yim AYF, van Gennep S, Hageman I, de Waard T, Levin E, Lauffer P, de Jonge W, Henneman P, Löwenberg M and D’Haens GR. Peripheral blood DNA methylation signatures and response to tofacitinib in moderate-to-severe ulcerative colitis. J Crohns Colitis 2024; 18: 1179-1189.","journal-title":"J Crohns Colitis"},{"key":"ref110","doi-asserted-by":"publisher","unstructured":"Waljee AK, Wallace BI, Cohen-Mekelburg S, Liu Y, Liu B, Sauder K, Stidham RW, Zhu J and Higgins PDR. Development and validation of machine learning models in prediction of remission in patients with moderate to severe Crohn disease. JAMA Netw Open 2019; 2: e193721.","DOI":"10.1001\/jamanetworkopen.2019.3721"},{"key":"ref111","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1002\/iid3.506","article-title":"Machine learning gene expression predicting model for ustekinumab response in patients with Crohn’s disease","volume":"9","author":"He","year":"2021","unstructured":"He M, Li C, Tang W, Kang Y, Zuo Y and Wang Y. Machine learning gene expression predicting model for ustekinumab response in patients with Crohn’s disease. Immun Inflamm Dis 2021; 9: 1529-1540.","journal-title":"Immun Inflamm Dis"},{"key":"ref112","doi-asserted-by":"publisher","first-page":"4518","DOI":"10.3390\/jcm11154518","article-title":"Using interpretable machine learning to identify baseline predictive factors of remission and drug durability in Crohn’s disease patients on ustekinumab","volume":"11","author":"Chaparro","year":"2022","unstructured":"Chaparro M, Baston-Rey I, Fernández Salgado E, González García J, Ramos L, Diz-Lois Palomares MT, Argüelles-Arias F, Iglesias Flores E, Cabello M, Rubio Iturria S, Núñez Ortiz A, Charro M, Ginard D, Dueñas Sadornil C, Merino Ochoa O, Busquets D, Iyo E, Gutiérrez Casbas A, Ramírez de la Piscina P, Boscá-Watts MM, Arroyo M, García MJ, Hinojosa E, Gordillo J, Martínez Montiel P, Velayos Jiménez B, Quílez Ivorra C, Vázquez Morón JM, Huguet JM, González-Lama Y, Muñagorri Santos AI, Amo VM, Martín Arranz MD, Bermejo F, Martínez Cadilla J, Rubín de Célix C, Fradejas Salazar P, López San Román A, Jiménez N, García-López S, Figuerola A, Jiménez I, Martínez Cerezo FJ, Taxonera C, Varela P, de Francisco R, Monfort D, Molina Arriero G, Hernández-Camba A, García Alonso FJ, Van Domselaar M, Pajares-Villarroya R, Núñez A, Rodríguez Moranta F, Marín-Jiménez I, Robles Alonso V, Martín Rodríguez MDM, Camo-Monterde P, García Tercero I, Navarro-Llavat M, García LA, Hervías Cruz D, Kloss S, Passey A, Novella C, Vispo E, Barreiro-de Acosta M and Gisbert JP. Using interpretable machine learning to identify baseline predictive factors of remission and drug durability in Crohn’s disease patients on ustekinumab. J Clin Med 2022; 11: 4518.","journal-title":"J Clin Med"},{"key":"ref113","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.dld.2022.07.015","article-title":"Predictive models assessing the response to ustekinumab highlight the value of therapeutic drug monitoring in Crohn’s disease","volume":"55","author":"Liefferinckx","year":"2023","unstructured":"Liefferinckx C, Hubert A, Thomas D, Bottieau J, Minsart C, Cremer A, Amininejad L, Vallée F, Toubeau JF and Franchimont D. Predictive models assessing the response to ustekinumab highlight the value of therapeutic drug monitoring in Crohn’s disease. Dig Liver Dis 2023; 55: 366-372.","journal-title":"Dig Liver Dis"},{"key":"ref114","doi-asserted-by":"publisher","first-page":"4386","DOI":"10.1038\/s41598-024-55126-1","article-title":"Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis","volume":"14","author":"Morikubo","year":"2024","unstructured":"Morikubo H, Tojima R, Maeda T, Matsuoka K, Matsuura M, Miyoshi J, Tamura S and Hisamatsu T. Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis. Sci Rep 2024; 14: 4386.","journal-title":"Sci Rep"},{"key":"ref115","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1111\/apt.14510","article-title":"Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis","volume":"47","author":"Waljee","year":"2018","unstructured":"Waljee AK, Liu B, Sauder K, Zhu J, Govani SM, Stidham RW and Higgins PDR. Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis. Aliment Pharmacol Ther 2018; 47: 763-772.","journal-title":"Aliment Pharmacol Ther"},{"key":"ref116","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1093\/ibd\/izy031","article-title":"Predicting corticosteroid-free biologic remission with vedolizumab in Crohn’s disease","volume":"24","author":"Waljee","year":"2018","unstructured":"Waljee AK, Liu B, Sauder K, Zhu J, Govani SM, Stidham RW and Higgins PDR. Predicting corticosteroid-free biologic remission with vedolizumab in Crohn’s disease. Inflamm Bowel Dis 2018; 24: 1185-1192.","journal-title":"Inflamm Bowel Dis"},{"key":"ref117","doi-asserted-by":"publisher","unstructured":"Dulai PS, Singh S, Vande Casteele N, Meserve J, Winters A, Chablaney S, Aniwan S, Shashi P, Kochhar G, Weiss A, Koliani-Pace JL, Gao Y, Boland BS, Chang JT, Faleck D, Hirten R, Ungaro R, Lukin D, Sultan K, Hudesman D, Chang S, Bohm M, Varma S, Fischer M, Shmidt E, Swaminath A, Gupta N, Rosario M, Jairath V, Guizzetti L, Feagan BG, Siegel CA, Shen B, Kane S, Loftus EV Jr, Sandborn WJ, Sands BE, Colombel JF, Lasch K and Cao C. Development and validation of clinical scoring tool to predict outcomes of treatment with vedolizumab in patients with ulcerative colitis. Clin Gastroenterol Hepatol 2020; 18: 2952-2961, e2958.","DOI":"10.1016\/j.cgh.2020.02.010"},{"key":"ref118","doi-asserted-by":"publisher","first-page":"16440","DOI":"10.1038\/s41598-021-96019-x","article-title":"Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis","volume":"11","author":"Miyoshi","year":"2021","unstructured":"Miyoshi J, Maeda T, Matsuoka K, Saito D, Miyoshi S, Matsuura M, Okamoto S, Tamura S and Hisamatsu T. Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis. Sci Rep 2021; 11: 16440.","journal-title":"Sci Rep"},{"key":"ref119","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1080\/10543406.2021.2009500","article-title":"Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients","volume":"32","author":"Chen","year":"2022","unstructured":"Chen J, Girard M, Wang S, Kisfalvi K and Lirio R. Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients. J Biopharm Stat 2022; 32: 330-345.","journal-title":"J Biopharm Stat"},{"key":"ref120","doi-asserted-by":"publisher","first-page":"3225","DOI":"10.1007\/s12325-022-02144-y","article-title":"A computational platform integrating a mechanistic model of Crohn’s disease for predicting temporal progression of mucosal damage and healing","volume":"39","author":"SP","year":"2022","unstructured":"Venkatapurapu SP, Iwakiri R, Udagawa E, Patidar N, Qi Z, Takayama R, Kumar K, Sato Y, Behar M, Offner P, Dwivedi G, Miyasaka H, Suzuki RK, Hamada AL, D’Alessandro PM and Fernandez J. A computational platform integrating a mechanistic model of Crohn’s disease for predicting temporal progression of mucosal damage and healing. Adv Ther 2022; 39: 3225-3247.","journal-title":"Adv Ther"},{"key":"ref121","doi-asserted-by":"publisher","first-page":"2667","DOI":"10.1038\/s41598-024-52678-0","article-title":"Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning","volume":"14","author":"Chen","year":"2024","unstructured":"Chen KA, Nishiyama NC, Kennedy Ng MM, Shumway A, Joisa CU, Schaner MR, Lian G, Beasley C, Zhu LC, Bantumilli S, Kapadia MR, Gomez SM, Furey TS and Sheikh SZ. Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning. Sci Rep 2024; 14: 2667.","journal-title":"Sci Rep"},{"key":"ref122","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1111\/jgh.13921","article-title":"Predictors of long-term outcomes in patients with acute severe colitis: a northern Indian cohort study","volume":"33","author":"Jain","year":"2018","unstructured":"Jain S, Kedia S, Sethi T, Bopanna S, Yadav DP, Goyal S, Padhan R, Venigalla PM, Sahni P, Dash NR, Pal S, Makharia G, Travis SPL and Ahuja V. Predictors of long-term outcomes in patients with acute severe colitis: a northern Indian cohort study. J Gastroenterol Hepatol 2018; 33: 615-622.","journal-title":"J Gastroenterol Hepatol"},{"key":"ref123","doi-asserted-by":"publisher","unstructured":"Dong Y, Xu L, Fan Y, Xiang P, Gao X, Chen Y, Zhang W and Ge Q. A novel surgical predictive model for Chinese Crohn’s disease patients. Medicine (Baltimore) 2019; 98: e17510.","DOI":"10.1097\/MD.0000000000017510"},{"key":"ref124","unstructured":"Sofo L, Caprino P, Schena CA, Sacchetti F, Potenza AE and Ciociola A. New perspectives in the prediction of postoperative complications for high-risk ulcerative colitis patients: machine learning preliminary approach. Eur Rev Med Pharmacol Sci 2020; 24: 12781-12787."},{"key":"ref125","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.1097\/MIB.0000000000000436","article-title":"Volatile organic compounds in exhaled air as novel marker for disease activity in Crohn’s disease: a metabolomic approach","volume":"21","author":"Bodelier","year":"2015","unstructured":"Bodelier AG, Smolinska A, Baranska A, Dallinga JW, Mujagic Z, Vanhees K, van den Heuvel T, Masclee AA, Jonkers D, Pierik MJ and van Schooten FJ. Volatile organic compounds in exhaled air as novel marker for disease activity in Crohn’s disease: a metabolomic approach. Inflamm Bowel Dis 2015; 21: 1776-1785.","journal-title":"Inflamm Bowel Dis"},{"key":"ref126","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1093\/ibd\/izx007","article-title":"Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning","volume":"24","author":"Waljee","year":"2017","unstructured":"Waljee AK, Lipson R, Wiitala WL, Zhang Y, Liu B, Zhu J, Wallace B, Govani SM, Stidham RW, Hayward R and Higgins PDR. Predicting hospitalization and outpatient corticosteroid use in inflammatory bowel disease patients using machine learning. Inflamm Bowel Dis 2017; 24: 45-53.","journal-title":"Inflamm Bowel Dis"},{"key":"ref127","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.14309\/ajg.0000000000000136","article-title":"Individualized dynamics in the gut microbiota precede Crohn’s disease flares","volume":"114","author":"Braun","year":"2019","unstructured":"Braun T, Di Segni A, BenShoshan M, Neuman S, Levhar N, Bubis M, Picard O, Sosnovski K, Efroni G, Farage Barhom S, Glick Saar E, Lahad A, Weiss B, Yablecovitch D, Lahat A, Eliakim R, Kopylov U, Ben-Horin S and Haberman Y. Individualized dynamics in the gut microbiota precede Crohn’s disease flares. Am J Gastroenterol 2019; 114: 1142-1151.","journal-title":"Am J Gastroenterol"},{"key":"ref128","doi-asserted-by":"publisher","unstructured":"Gan RW, Sun D, Tatro AR, Cohen-Mekelburg S, Wiitala WL, Zhu J and Waljee AK. Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease. PLoS One 2021; 16: e0257520.","DOI":"10.1371\/journal.pone.0257520"},{"key":"ref129","doi-asserted-by":"publisher","first-page":"276","DOI":"10.5152\/tjg.2021.20420","article-title":"Developing a neural network model for a non-invasive prediction of histologic activity in inflammatory bowel diseases","volume":"32","author":"Popa","year":"2021","unstructured":"Popa IV, Diculescu M, Mihai C, Prelipcean CC and Burlacu A. Developing a neural network model for a non-invasive prediction of histologic activity in inflammatory bowel diseases. Turk J Gastroenterol 2021; 32: 276-286.","journal-title":"Turk J Gastroenterol"},{"key":"ref130","doi-asserted-by":"publisher","first-page":"14133","DOI":"10.1007\/s00521-021-06055-x","article-title":"A new approach to predict ulcerative colitis activity through standard clinical-biological parameters using a robust neural network model","volume":"33","author":"Popa","year":"2021","unstructured":"Popa IV, Burlacu A, Gavrilescu O, Dranga M, Prelipcean CC and Mihai C. A new approach to predict ulcerative colitis activity through standard clinical-biological parameters using a robust neural network model. Neural Computing and Applications 2021; 33: 14133-14146.","journal-title":"Neural Computing and Applications"},{"key":"ref131","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1097\/MEG.0000000000002317","article-title":"Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study","volume":"34","author":"Gomoll&#xF3","year":"2022","unstructured":"Gomollón F, Gisbert JP, Guerra I, Plaza R, Pajares Villarroya R, Moreno Almazán L, López Martín MC, Domínguez Antonaya M, Vera Mendoza MI, Aparicio J, Martínez V, Tagarro I, Fernández-Nistal A, Lumbreras S, Maté C and Montoto C; Premonition-CD Study Group. Clinical characteristics and prognostic factors for Crohn’s disease relapses using natural language processing and machine learning: a pilot study. Eur J Gastroenterol Hepatol 2022; 34: 389-397.","journal-title":"Eur J Gastroenterol Hepatol"},{"key":"ref132","doi-asserted-by":"publisher","first-page":"2028366","DOI":"10.1080\/19490976.2022.2028366","article-title":"A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach","volume":"14","author":"Barberio","year":"2022","unstructured":"Barberio B, Facchin S, Patuzzi I, Ford AC, Massimi D, Valle G, Sattin E, Simionati B, Bertazzo E, Zingone F and Savarino EV. A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach. Gut Microbes 2022; 14: 2028366.","journal-title":"Gut Microbes"},{"key":"ref133","doi-asserted-by":"publisher","first-page":"1043412","DOI":"10.3389\/fmed.2022.1043412","article-title":"Predictive models for endoscopic disease activity in patients with ulcerative colitis: practical machine learning-based modeling and interpretation","volume":"9","author":"Li","year":"2022","unstructured":"Li X, Yan L, Wang X, Ouyang C, Wang C, Chao J, Zhang J and Lian G. Predictive models for endoscopic disease activity in patients with ulcerative colitis: practical machine learning-based modeling and interpretation. Front Med (Lausanne) 2022; 9: 1043412.","journal-title":"Front Med (Lausanne)"},{"key":"ref134","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1002\/ueg2.12268","article-title":"LIAISON(®) Calprotectin for the prediction of relapse in quiescent ulcerative colitis: the EuReCa study","volume":"10","author":"Fiorino","year":"2022","unstructured":"Fiorino G, Danese S, Peyrin-Biroulet L, Sans M, Bonelli F, Calleri M, Zierold C, Pollastro R, Moretti F and Malesci A. LIAISON(®) Calprotectin for the prediction of relapse in quiescent ulcerative colitis: the EuReCa study. United European Gastroenterol J 2022; 10: 836-843.","journal-title":"United European Gastroenterol J"},{"key":"ref135","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1007\/s10753-023-01827-0","article-title":"Performance of machine learning algorithms for predicting disease activity in inflammatory bowel disease","volume":"46","author":"Cai","year":"2023","unstructured":"Cai W, Xu J, Chen Y, Wu X, Zeng Y and Yu F. Performance of machine learning algorithms for predicting disease activity in inflammatory bowel disease. Inflammation 2023; 46: 1561-1574.","journal-title":"Inflammation"},{"key":"ref136","doi-asserted-by":"publisher","first-page":"1101237","DOI":"10.3389\/fmed.2023.1101237","article-title":"Risk prediction model based on blood biomarkers for predicting moderate to severe endoscopic activity in patients with ulcerative colitis","volume":"10","author":"Li","year":"2023","unstructured":"Li X, Tang Z, Liu Y, Zhu X and Liu F. Risk prediction model based on blood biomarkers for predicting moderate to severe endoscopic activity in patients with ulcerative colitis. Front Med (Lausanne) 2023; 10: 1101237.","journal-title":"Front Med (Lausanne)"},{"key":"ref137","doi-asserted-by":"publisher","first-page":"3531","DOI":"10.2147\/JIR.S423086","article-title":"Serological biomarker-based machine learning models for predicting the relapse of ulcerative colitis","volume":"16","author":"Pang","year":"2023","unstructured":"Pang W, Zhang B, Jin L, Yao Y, Han Q and Zheng X. Serological biomarker-based machine learning models for predicting the relapse of ulcerative colitis. J Inflamm Res 2023; 16: 3531-3545.","journal-title":"J Inflamm Res"},{"key":"ref138","doi-asserted-by":"publisher","first-page":"3609","DOI":"10.3390\/jcm12113609","article-title":"Laboratory data and IBDQ-effective predictors for the non-invasive machine-learning-based prediction of endoscopic activity in ulcerative colitis","volume":"12","author":"Gavrilescu","year":"2023","unstructured":"Gavrilescu O, Popa IV, Dranga M, Mihai R, Cijevschi Prelipcean C and Mihai C. Laboratory data and IBDQ-effective predictors for the non-invasive machine-learning-based prediction of endoscopic activity in ulcerative colitis. J Clin Med 2023; 12: 3609.","journal-title":"J Clin Med"},{"key":"ref139","doi-asserted-by":"publisher","unstructured":"Jangi S, Hsia K, Zhao N, Kumamoto CA, Friedman S, Singh S and Michaud DS. Dynamics of the gut mycobiome in patients with ulcerative colitis. Clin Gastroenterol Hepatol 2024; 22: 821-830, e827.","DOI":"10.1016\/j.cgh.2023.09.023"},{"key":"ref140","doi-asserted-by":"publisher","first-page":"4170","DOI":"10.1172\/JCI59255","article-title":"Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis","volume":"121","author":"Lee","year":"2011","unstructured":"Lee JC, Lyons PA, McKinney EF, Sowerby JM, Carr EJ, Bredin F, Rickman HM, Ratlamwala H, Hatton A, Rayner TF, Parkes M and Smith KG. Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis. J Clin Invest 2011; 121: 4170-4179.","journal-title":"J Clin Invest"},{"key":"ref141","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1136\/gutjnl-2019-318343","article-title":"A blood-based prognostic biomarker in IBD","volume":"68","author":"Biasci","year":"2019","unstructured":"Biasci D, Lee JC, Noor NM, Pombal DR, Hou M, Lewis N, Ahmad T, Hart A, Parkes M, McKinney EF, Lyons PA and Smith KGC. A blood-based prognostic biomarker in IBD. Gut 2019; 68: 1386-1395.","journal-title":"Gut"},{"key":"ref142","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1111\/apt.16136","article-title":"Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn’s disease","volume":"53","author":"Ungaro","year":"2021","unstructured":"Ungaro RC, Hu L, Ji J, Nayar S, Kugathasan S, Denson LA, Hyams J, Dubinsky MC, Sands BE and Cho JH. Machine learning identifies novel blood protein predictors of penetrating and stricturing complications in newly diagnosed paediatric Crohn’s disease. Aliment Pharmacol Ther 2021; 53: 281-290.","journal-title":"Aliment Pharmacol Ther"},{"key":"ref143","doi-asserted-by":"publisher","unstructured":"Wang J, Ortiz C, Fontenot L, Xie Y, Ho W, Mattai SA, Shih DQ and Koon HW. High circulating elafin levels are associated with Crohn’s disease-associated intestinal strictures. PLoS One 2020; 15: e0231796.","DOI":"10.1371\/journal.pone.0231796"},{"key":"ref144","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1093\/ibd\/izaa281","article-title":"Understanding the molecular drivers of disease heterogeneity in Crohn’s disease using multi-omic data integration and network analysis","volume":"27","author":"Sudhakar","year":"2021","unstructured":"Sudhakar P, Verstockt B, Cremer J, Verstockt S, Sabino J, Ferrante M and Vermeire S. Understanding the molecular drivers of disease heterogeneity in Crohn’s disease using multi-omic data integration and network analysis. Inflamm Bowel Dis 2021; 27: 870-886.","journal-title":"Inflamm Bowel Dis"},{"key":"ref145","doi-asserted-by":"publisher","first-page":"17562848211053114","DOI":"10.1177\/17562848211053114","article-title":"Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department","volume":"14","author":"Levartovsky","year":"2021","unstructured":"Levartovsky A, Barash Y, Ben-Horin S, Ungar B, Soffer S, Amitai MM, Klang E and Kopylov U. Machine learning for prediction of intra-abdominal abscesses in patients with Crohn’s disease visiting the emergency department. Therap Adv Gastroenterol 2021; 14: 17562848211053114.","journal-title":"Therap Adv Gastroenterol"},{"key":"ref146","unstructured":"Ma JL, Zhang HJ, Zhang CF, Zhang YY and Wang GM. Construction of molecular subgroups of ulcerative colitis. Eur Rev Med Pharmacol Sci 2023; 27: 9333-9345."},{"key":"ref147","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1093\/ecco-jcc\/jjad011","article-title":"A compendium of mucosal molecular characteristics provides novel perspectives on the treatment of ulcerative colitis","volume":"17","author":"Chang","year":"2023","unstructured":"Chang MJ, Hao JW, Qiao J, Chen MR, Wang Q, Wang Q, Zhang SX, Yu Q and He PF. A compendium of mucosal molecular characteristics provides novel perspectives on the treatment of ulcerative colitis. J Crohns Colitis 2023; 17: 909-918.","journal-title":"J Crohns Colitis"},{"key":"ref148","doi-asserted-by":"publisher","first-page":"10467","DOI":"10.3390\/ijms231810467","article-title":"Peripheral blood DNA methylation profiles do not predict endoscopic post-operative recurrence in Crohn’s disease patients","volume":"23","author":"VW","year":"2022","unstructured":"Joustra VW, Li Yim AYF, de Bruyn JR, Duijvestein M, Hageman IL, de Jonge WJ, Henneman P, Wildenberg M and D’Haens G. Peripheral blood DNA methylation profiles do not predict endoscopic post-operative recurrence in Crohn’s disease patients. Int J Mol Sci 2022; 23: 10467.","journal-title":"Int J Mol Sci"},{"key":"ref149","doi-asserted-by":"publisher","first-page":"84","DOI":"10.2174\/1389202924666230601122334","article-title":"Identification of potential genes and critical pathways in postoperative recurrence of Crohn’s disease by machine learning and WGCNA network analysis","volume":"24","author":"Rajalingam","year":"2023","unstructured":"Rajalingam A, Sekar K and Ganjiwale A. Identification of potential genes and critical pathways in postoperative recurrence of Crohn’s disease by machine learning and WGCNA network analysis. Curr Genomics 2023; 24: 84-99.","journal-title":"Curr Genomics"},{"key":"ref150","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1093\/ibd\/izy228","article-title":"Predicting risk of postoperative disease recurrence in Crohn’s disease: patients with indolent Crohn’s disease have distinct whole transcriptome profiles at the time of first surgery","volume":"25","author":"Cushing","year":"2019","unstructured":"Cushing KC, McLean R, McDonald KG, Gustafsson JK, Knoop KA, Kulkarni DH, Sartor RB and Newberry RD. Predicting risk of postoperative disease recurrence in Crohn’s disease: patients with indolent Crohn’s disease have distinct whole transcriptome profiles at the time of first surgery. Inflamm Bowel Dis 2019; 25: 180-193.","journal-title":"Inflamm Bowel Dis"},{"key":"ref151","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1093\/ibd\/izx070","article-title":"A distinctive urinary metabolomic fingerprint is linked with endoscopic postoperative disease recurrence in Crohn’s disease patients","volume":"24","author":"Keshteli","year":"2018","unstructured":"Keshteli AH, Tso R, Dieleman LA, Park H, Kroeker KI, Jovel J, Gillevet PM, Sikaroodi M, Mandal R, Fedorak RN and Madsen KL. A distinctive urinary metabolomic fingerprint is linked with endoscopic postoperative disease recurrence in Crohn’s disease patients. Inflamm Bowel Dis 2018; 24: 861-870.","journal-title":"Inflamm Bowel Dis"},{"key":"ref152","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1136\/gutjnl-2019-318719","article-title":"Prominence of ileal mucosa-associated microbiota to predict postoperative endoscopic recurrence in Crohn’s disease","volume":"69","author":"Sokol","year":"2020","unstructured":"Sokol H, Brot L, Stefanescu C, Auzolle C, Barnich N, Buisson A, Fumery M, Pariente B, Le Bourhis L, Treton X, Nancey S, Allez M and Seksik P; REMIND Study Group Investigators. Prominence of ileal mucosa-associated microbiota to predict postoperative endoscopic recurrence in Crohn’s disease. Gut 2020; 69: 462-472.","journal-title":"Gut"},{"key":"ref153","doi-asserted-by":"publisher","first-page":"656","DOI":"10.3390\/ijerph20010656","article-title":"Machine learning model in predicting sarcopenia in Crohn’s disease based on simple clinical and anthropometric measures","volume":"20","author":"Tseng","year":"2022","unstructured":"Tseng Y, Mo S, Zeng Y, Zheng W, Song H, Zhong B, Luo F, Rong L, Liu J and Luo Z. Machine learning model in predicting sarcopenia in Crohn’s disease based on simple clinical and anthropometric measures. Int J Environ Res Public Health 2022; 20: 656.","journal-title":"Int J Environ Res Public Health"},{"key":"ref154","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.3389\/fmicb.2017.01162","article-title":"Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity","volume":"8","author":"VK","year":"2017","unstructured":"Gupta VK, Paul S and Dutta C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front Microbiol 2017; 8: 1162.","journal-title":"Front Microbiol"},{"key":"ref155","unstructured":"Sundararajan M and Najmi A. The many Shapley values for model explanation. ICML 2020; 119: 9269-9278."},{"key":"ref156","doi-asserted-by":"publisher","unstructured":"Misra BB, Langefeld C, Olivier M and Cox LA. Integrated omics: tools, advances and future approaches. J Mol Endocrinol 2019; 62: R21-R45.","DOI":"10.1530\/JME-18-0055"},{"key":"ref157","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1111\/apt.17370","article-title":"Systematic review with meta-analysis: time to diagnosis and the impact of delayed diagnosis on clinical outcomes in inflammatory bowel disease","volume":"57","author":"Jayasooriya","year":"2023","unstructured":"Jayasooriya N, Baillie S, Blackwell J, Bottle A, Petersen I, Creese H, Saxena S and Pollok RC; POP-IBD study group. Systematic review with meta-analysis: time to diagnosis and the impact of delayed diagnosis on clinical outcomes in inflammatory bowel disease. Aliment Pharmacol Ther 2023; 57: 635-652.","journal-title":"Aliment Pharmacol Ther"},{"key":"ref158","doi-asserted-by":"publisher","unstructured":"Lamb CA, Kennedy NA, Raine T, Hendy PA, Smith PJ, Limdi JK, Hayee B, Lomer MCE, Parkes GC, Selinger C, Barrett KJ, Davies RJ, Bennett C, Gittens S, Dunlop MG, Faiz O, Fraser A, Garrick V, Johnston PD, Parkes M, Sanderson J, Terry H; IBD guidelines eDelphi consensus group; Gaya DR, Iqbal TH, Taylor SA, Smith M, Brookes M, Hansen R and Hawthorne AB. British Society of Gastroenterology consensus guidelines on the management of inflammatory bowel disease in adults. Gut 2019; 68 Suppl 3: s1-s106.","DOI":"10.1136\/gutjnl-2019-318484"},{"key":"ref159","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1097\/MPG.0000000000002035","article-title":"Management of paediatric ulcerative colitis, part 1: ambulatory care-an evidence-based Guideline From European Crohn’s and Colitis Organization and European Society of Paediatric Gastroenterology, Hepatology and Nutrition","volume":"67","author":"Turner","year":"2018","unstructured":"Turner D, Ruemmele FM, Orlanski-Meyer E, Griffiths AM, de Carpi JM, Bronsky J, Veres G, Aloi M, Strisciuglio C, Braegger CP, Assa A, Romano C, Hussey S, Stanton M, Pakarinen M, de Ridder L, Katsanos K, Croft N, Navas-López V, Wilson DC, Lawrence S and Russell RK. Management of paediatric ulcerative colitis, part 1: ambulatory care-an evidence-based Guideline From European Crohn’s and Colitis Organization and European Society of Paediatric Gastroenterology, Hepatology and Nutrition. J Pediatr Gastroenterol Nutr 2018; 67: 257-291.","journal-title":"J Pediatr Gastroenterol Nutr"},{"key":"ref160","doi-asserted-by":"publisher","unstructured":"van Rheenen PF, Aloi M, Assa A, Bronsky J, Escher JC, Fagerberg UL, Gasparetto M, Gerasimidis K, Griffiths A, Henderson P, Koletzko S, Kolho KL, Levine A, van Limbergen J, Martin de Carpi FJ, Navas-López VM, Oliva S, de Ridder L, Russell RK, Shouval D, Spinelli A, Turner D, Wilson D, Wine E and Ruemmele FM. The medical management of paediatric Crohn’s disease: an ECCO-ESPGHAN guideline update. J Crohns Colitis 2020; jjaa161.","DOI":"10.1093\/ecco-jcc\/jjaa161"},{"key":"ref161","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1016\/j.cgh.2022.03.011","article-title":"The impact of the social determinants of health on disparities in inflammatory bowel disease","volume":"20","author":"Anyane-Yeboa","year":"2022","unstructured":"Anyane-Yeboa A, Quezada S, Rubin DT and Balzora S. The impact of the social determinants of health on disparities in inflammatory bowel disease. Clin Gastroenterol Hepatol 2022; 20: 2427-2434.","journal-title":"Clin Gastroenterol Hepatol"},{"key":"ref162","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/S2468-1253(24)00034-7","article-title":"A biomarker-stratified comparison of top-down versus accelerated step-up treatment strategies for patients with newly diagnosed Crohn’s disease (PROFILE): a multicentre, open-label randomised controlled trial","volume":"9","author":"NM","year":"2024","unstructured":"Noor NM, Lee JC, Bond S, Dowling F, Brezina B, Patel KV, Ahmad T, Banim PJ, Berrill JW, Cooney R, De La Revilla Negro J, de Silva S, Din S, Durai D, Gordon JN, Irving PM, Johnson M, Kent AJ, Kok KB, Moran GW, Mowat C, Patel P, Probert CS, Raine T, Saich R, Seward A, Sharpstone D, Smith MA, Subramanian S, Upponi SS, Wiles A, Williams HRT, van den Brink GR, Vermeire S, Jairath V, D’Haens GR, McKinney EF, Lyons PA, Lindsay JO, Kennedy NA, Smith KGC and Parkes M; PROFILE Study Group. A biomarker-stratified comparison of top-down versus accelerated step-up treatment strategies for patients with newly diagnosed Crohn’s disease (PROFILE): a multicentre, open-label randomised controlled trial. Lancet Gastroenterol Hepatol 2024; 9: 415-427.","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"ref163","doi-asserted-by":"publisher","unstructured":"Wyatt NJ, Watson H, Anderson CA, Kennedy NA, Raine T, Ahmad T, Allerton D, Bardgett M, Clark E, Clewes D, Cotobal Martin C, Doona M, Doyle JA, Frith K, Hancock HC, Hart AL, Hildreth V, Irving PM, Iqbal S, Kennedy C, King A, Lawrence S, Lees CW, Lees R, Letchford L, Liddle T, Lindsay JO, Maier RH, Mansfield JC, Marchesi JR, McGregor N, McIntyre RE, Ostermayer J, Osunnuyi T, Powell N, Prescott NJ, Satsangi J, Sharma S, Shrestha T, Speight A, Strickland M, Wason JM, Whelan K, Wood R, Young GR, Zhang X, Parkes M, Stewart CJ, Jostins-Dean L and Lamb CA. Defining predictors of responsiveness to advanced therapies in Crohn’s disease and ulcerative colitis: protocol for the IBD-RESPONSE and nested CD-metaRESPONSE prospective, multicentre, observational cohort study in precision medicine. BMJ Open 2024; 14: e073639.","DOI":"10.1136\/bmjopen-2023-073639"}],"container-title":["American Journal of Translational Research"],"original-title":[],"link":[{"URL":"https:\/\/e-century.us\/files\/ajtr\/17\/1\/ajtr0158947.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T01:55:59Z","timestamp":1766022959000},"score":1,"resource":{"primary":{"URL":"https:\/\/e-century.us\/files\/ajtr\/17\/1\/ajtr0158947.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":164,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.62347\/xill3707","relation":{},"ISSN":["1943-8141"],"issn-type":[{"value":"1943-8141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}