{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T09:40:07Z","timestamp":1748943607062,"version":"3.41.0"},"reference-count":91,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-025-00533-1","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T20:49:11Z","timestamp":1748897351000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating relieff-based feature selection and ensemble machine learning for robust biomarker identification in colorectal cancer"],"prefix":"10.1007","volume":"14","author":[{"given":"Pritam","family":"Bera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subarna","family":"Debnath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5802-7474","authenticated-orcid":false,"given":"Chittabrata","family":"Mal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil Kanti","family":"Mondal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"issue":"1","key":"533_CR1","doi-asserted-by":"publisher","first-page":"19426","DOI":"10.1038\/s41598-023-46633-8","volume":"13","author":"A Ahmadieh-Yazdi","year":"2023","unstructured":"Ahmadieh-Yazdi A, Mahdavinezhad A, Tapak L, Nouri F, Taherkhani A, Afshar S (2023) Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation. Sci Rep 13(1):19426. https:\/\/doi.org\/10.1038\/s41598-023-46633-8","journal-title":"Sci Rep"},{"issue":"2","key":"533_CR2","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3390\/bioengineering10020173","volume":"10","author":"F Alharbi","year":"2023","unstructured":"Alharbi F, Vakanski A (2023) Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering 10(2):173. https:\/\/doi.org\/10.3390\/bioengineering10020173","journal-title":"Bioengineering"},{"issue":"9","key":"533_CR3","doi-asserted-by":"publisher","first-page":"7781","DOI":"10.3390\/ijms24097781","volume":"24","author":"Q Al-Tashi","year":"2023","unstructured":"Al-Tashi Q, Saad MB, Muneer A, Qureshi R, Mirjalili S, Sheshadri A, Le X, Vokes NI, Zhang J, Wu J (2023) Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. Int J Mol Sci 24(9):7781. https:\/\/doi.org\/10.3390\/ijms24097781","journal-title":"Int J Mol Sci"},{"issue":"1","key":"533_CR4","first-page":"19","volume":"5","author":"T Armaghany","year":"2012","unstructured":"Armaghany T, Wilson JD, Chu Q, Mills G (2012) Genetic alterations in colorectal cancer. Gastrointestinal Cancer Research GCR 5(1):19\u201327","journal-title":"Gastrointestinal Cancer Research GCR"},{"issue":"1","key":"533_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e41443","volume":"11","author":"E Ayubi","year":"2025","unstructured":"Ayubi E, Farashi S, Tapak L, Afshar S (2025) Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms. Heliyon 11(1):e41443. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e41443","journal-title":"Heliyon"},{"issue":"2","key":"533_CR6","doi-asserted-by":"publisher","first-page":"204","DOI":"10.5009\/gnl15420","volume":"10","author":"JR Bailey","year":"2016","unstructured":"Bailey JR, Aggarwal A, Imperiale TF (2016) Colorectal Cancer Screening: Stool DNA and Other Noninvasive Modalities. Gut and Liver 10(2):204\u2013211. https:\/\/doi.org\/10.5009\/gnl15420","journal-title":"Gut and Liver"},{"key":"533_CR7","doi-asserted-by":"publisher","unstructured":"Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229\u2013263. https:\/\/doi.org\/10.3322\/caac.21834","DOI":"10.3322\/caac.21834"},{"issue":"1","key":"533_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random Forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"533_CR9","doi-asserted-by":"publisher","unstructured":"Bumgarner R. (2013). Overview of DNA microarrays: types, applications, and their future. Current protocols in molecular biology, Chapter 22, Unit\u201322.1. https:\/\/doi.org\/10.1002\/0471142727.mb2201s101","DOI":"10.1002\/0471142727.mb2201s101"},{"issue":"5","key":"533_CR10","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1586\/14737159.4.5.599","volume":"4","author":"SA Bustin","year":"2004","unstructured":"Bustin SA, Dorudi S (2004) Gene expression profiling for molecular staging and prognosis prediction in colorectal cancer. Expert Rev Mol Diagn 4(5):599\u2013607. https:\/\/doi.org\/10.1586\/14737159.4.5.599","journal-title":"Expert Rev Mol Diagn"},{"key":"533_CR11","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"Journal of Artificial Intelligence Research"},{"key":"533_CR12","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C.(2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785\u2013794. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"533_CR13","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/634123","author":"CM Chu","year":"2014","unstructured":"Chu CM, Yao CT, Chang YT, Chou HL, Chou YC, Chen KH, Terng HJ, Huang CS, Lee CC, Su SL, Liu YC, Lin FG, Wetter T, Chang CW (2014) Gene expression profiling of colorectal tumors and normal mucosa by microarrays meta-analysis using prediction analysis of microarray, artificial neural network, classification, and regression trees. Dis Markers. https:\/\/doi.org\/10.1155\/2014\/634123","journal-title":"Dis Markers"},{"issue":"1","key":"533_CR14","doi-asserted-by":"publisher","first-page":"388","DOI":"10.3892\/ol.2019.11068","volume":"19","author":"GP Dai","year":"2020","unstructured":"Dai GP, Wang LP, Wen YQ, Ren XQ, Zuo SG (2020) Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis. Oncol Lett 19(1):388\u2013398. https:\/\/doi.org\/10.3892\/ol.2019.11068","journal-title":"Oncol Lett"},{"issue":"91","key":"533_CR15","doi-asserted-by":"publisher","first-page":"36392","DOI":"10.18632\/oncotarget.26351","volume":"9","author":"S Dajani","year":"2018","unstructured":"Dajani S, Saripalli A, Sharma-Walia N (2018) Water transport proteins-aquaporins (AQPs) in cancer biology. Oncotarget 9(91):36392\u201336405. https:\/\/doi.org\/10.18632\/oncotarget.26351","journal-title":"Oncotarget"},{"key":"533_CR16","doi-asserted-by":"crossref","unstructured":"Duan, B., Zhao, Y., Bai, J., & others. (2022). Colorectal cancer: An overview. In J. A. Morgado-Diaz (Ed.), Gastrointestinal cancers (Chapter 1). Exon Publications. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK586003\/","DOI":"10.36255\/exon-publications-gastrointestinal-cancers-colorectal-cancer"},{"issue":"1","key":"533_CR17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/nar\/30.1.207","volume":"30","author":"R Edgar","year":"2002","unstructured":"Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207\u2013210. https:\/\/doi.org\/10.1093\/nar\/30.1.207","journal-title":"Nucleic Acids Res"},{"key":"533_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1472-6793-1-1","volume":"1","author":"H Fischer","year":"2001","unstructured":"Fischer H, Stenling R, Rubio C, Lindblom A (2001) Differential expression of aquaporin 8 in human colonic epithelial cells and colorectal tumors. BMC Physiol 1:1. https:\/\/doi.org\/10.1186\/1472-6793-1-1","journal-title":"BMC Physiol"},{"key":"533_CR19","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-4870116\/v1","author":"PJ Freda","year":"2024","unstructured":"Freda PJ, Ye S, Zhang R, Moore JH, Urbanowicz RJ (2024) Assessing the limitations of relief-based algorithms in detecting higher-order interactions. Res Square. https:\/\/doi.org\/10.21203\/rs.3.rs-4870116\/v1","journal-title":"Res Square"},{"key":"533_CR20","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.2147\/OTT.S98910","volume":"9","author":"MN Gabere","year":"2016","unstructured":"Gabere MN, Hussein MA, Aziz MA (2016) Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer. Onco Targets Ther 9:3313\u20133325. https:\/\/doi.org\/10.2147\/OTT.S98910","journal-title":"Onco Targets Ther"},{"issue":"3","key":"533_CR21","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1093\/bioinformatics\/btg405","volume":"20","author":"L Gautier","year":"2004","unstructured":"Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) affy\u2013analysis of Affymetrix GeneChip data at the probe level. Bioinformatics (Oxford, England) 20(3):307\u2013315. https:\/\/doi.org\/10.1093\/bioinformatics\/btg405","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"8","key":"533_CR22","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1093\/bioinformatics\/btz931","volume":"36","author":"SX Ge","year":"2020","unstructured":"Ge SX, Jung D, Yao R (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics (Oxford, England) 36(8):2628\u20132629. https:\/\/doi.org\/10.1093\/bioinformatics\/btz931","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"19","key":"533_CR23","doi-asserted-by":"publisher","first-page":"2811","DOI":"10.1093\/bioinformatics\/btu393","volume":"30","author":"Z Gu","year":"2014","unstructured":"Gu Z, Gu L, Eils R, Schlesner M, Brors B (2014) circlize implements and enhances circular visualization in R. Bioinformatics 30(19):2811\u20132812. https:\/\/doi.org\/10.1093\/bioinformatics\/btu393","journal-title":"Bioinformatics"},{"key":"533_CR24","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.2147\/CMAR.S345299","volume":"14","author":"S Guo","year":"2022","unstructured":"Guo S, Sun Y (2022) OTOP2, inversely modulated by miR-3148, inhibits CRC cell migration, proliferation and epithelial-mesenchymal transition: evidence from bioinformatics data mining and experimental verification. Cancer Manage Res 14:1371\u20131384. https:\/\/doi.org\/10.2147\/CMAR.S345299","journal-title":"Cancer Manage Res"},{"issue":"4","key":"533_CR25","doi-asserted-by":"publisher","first-page":"722","DOI":"10.3390\/ijms18040722","volume":"18","author":"Y Guo","year":"2017","unstructured":"Guo Y, Bao Y, Ma M, Yang W (2017) Identification of key candidate genes and pathways in colorectal cancer by integrated bioinformatical analysis. Int J Mol Sci 18(4):722. https:\/\/doi.org\/10.3390\/ijms18040722","journal-title":"Int J Mol Sci"},{"issue":"1","key":"533_CR26","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s40537-024-00973-y","volume":"11","author":"RK Halder","year":"2024","unstructured":"Halder RK, Uddin MN, Uddin MA, Aryal S, Khraisat A (2024) Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications. Journal of Big Data 11(1):113. https:\/\/doi.org\/10.1186\/s40537-024-00973-y","journal-title":"Journal of Big Data"},{"issue":"6","key":"533_CR27","doi-asserted-by":"publisher","first-page":"8997","DOI":"10.3934\/mbe.2021443","volume":"18","author":"A Hammad","year":"2021","unstructured":"Hammad A, Elshaer M, Tang X (2021) Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning. Math Biosci Eng MBE 18(6):8997\u20139015. https:\/\/doi.org\/10.3934\/mbe.2021443","journal-title":"Math Biosci Eng MBE"},{"key":"533_CR28","doi-asserted-by":"publisher","first-page":"D258","DOI":"10.1093\/nar\/gkh036","volume":"32","author":"MA Harris","year":"2004","unstructured":"Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M et al (2004) The Gene Ontology (GO) database and informatics resource. Nucl Acids Res 32:D258\u2013D261. https:\/\/doi.org\/10.1093\/nar\/gkh036","journal-title":"Nucl Acids Res"},{"issue":"4","key":"533_CR29","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.compbiolchem.2010.07.002","volume":"34","author":"Z He","year":"2010","unstructured":"He Z, Yu W (2010) Stable feature selection for biomarker discovery. Comput Biol Chem 34(4):215\u2013225. https:\/\/doi.org\/10.1016\/j.compbiolchem.2010.07.002","journal-title":"Comput Biol Chem"},{"key":"533_CR30","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.632834","author":"J He","year":"2021","unstructured":"He J, Wu F, Han Z, Hu M, Lin W, Li Y, Cao M (2021) Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of colorectal cancer\u2014From the body fluid to tissue level. Front Oncol. https:\/\/doi.org\/10.3389\/fonc.2021.632834","journal-title":"Front Oncol"},{"issue":"11","key":"533_CR31","first-page":"5271","volume":"13","author":"JM Hu","year":"2023","unstructured":"Hu JM, Liu PY, Chen YC, Lin WZ, Chou YC, Tsai WC, Chu CM, Wu CC, Chang YT (2023) A co-regulatory network of SPIB, AQP8, and GUCA2B related to immune infiltration for early-stage colorectal cancer in silico and in vitro. Am J Cancer Res 13(11):5271\u20135288","journal-title":"Am J Cancer Res"},{"issue":"1","key":"533_CR32","doi-asserted-by":"publisher","first-page":"41","DOI":"10.21873\/cgp.20063","volume":"15","author":"S Huang","year":"2018","unstructured":"Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteom 15(1):41\u201351. https:\/\/doi.org\/10.21873\/cgp.20063","journal-title":"Cancer Genom Proteom"},{"issue":"2","key":"533_CR33","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1093\/biostatistics\/4.2.249","volume":"4","author":"RA Irizarry","year":"2003","unstructured":"Irizarry RA (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249\u2013264. https:\/\/doi.org\/10.1093\/biostatistics\/4.2.249","journal-title":"Biostatistics"},{"issue":"2","key":"533_CR34","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3390\/computers8020042","volume":"8","author":"I Jo","year":"2019","unstructured":"Jo I, Lee S, Oh S (2019) Improved measures of redundancy and relevance for mRMR feature selection. Computers 8(2):42. https:\/\/doi.org\/10.3390\/computers8020042","journal-title":"Computers"},{"key":"533_CR35","doi-asserted-by":"publisher","first-page":"27522","DOI":"10.1109\/ACCESS.2022.3146312","volume":"10","author":"M Khalsan","year":"2022","unstructured":"Khalsan M, Machado LR, Al-Shamery ES, Ajit S, Anthony K, Mu M, Agyeman MO (2022) A survey of machine learning approaches applied to gene expression analysis for cancer prediction. IEEE Access 10:27522\u201327534. https:\/\/doi.org\/10.1109\/ACCESS.2022.3146312","journal-title":"IEEE Access"},{"issue":"45","key":"533_CR36","doi-asserted-by":"publisher","first-page":"16964","DOI":"10.3748\/wjg.v20.i45.16964","volume":"20","author":"S Kijima","year":"2014","unstructured":"Kijima S, Sasaki T, Nagata K, Utano K, Lefor AT, Sugimoto H (2014) Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET\/CT. World J Gastroenterol 20(45):16964\u201316975. https:\/\/doi.org\/10.3748\/wjg.v20.i45.16964","journal-title":"World J Gastroenterol"},{"key":"533_CR37","unstructured":"Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. In: Proceedings of the tenth national conference on artificial intelligence, pp. 129\u2013134. AAAI Press"},{"issue":"3","key":"533_CR38","doi-asserted-by":"publisher","first-page":"365","DOI":"10.3390\/biology11030365","volume":"11","author":"S Koppad","year":"2022","unstructured":"Koppad S, Basava A, Nash K, Gkoutos GV, Acharjee A (2022) Machine learning-based identification of colon cancer candidate diagnostics genes. Biology 11(3):365. https:\/\/doi.org\/10.3390\/biology11030365","journal-title":"Biology"},{"key":"533_CR39","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-42227-1_5","volume-title":"Computational intelligence: a methodological introduction","author":"R Kruse","year":"2022","unstructured":"Kruse R, Mostaghim S, Borgelt C, Braune C, Steinbrecher M (2022) Multi-layer perceptrons. In: Kruse R, Mostaghim S, Borgelt C, Braune C, Steinbrecher M (eds) Computational intelligence: a methodological introduction. Springer International Publishing, New York, pp 53\u2013124"},{"issue":"12","key":"533_CR40","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.3390\/biomedicines9121937","volume":"9","author":"A Lacalamita","year":"2021","unstructured":"Lacalamita A, Piccinno E, Scalavino V, Bellotti R, Giannelli G, Serino G (2021) A gene-based machine learning classifier associated to the colorectal adenoma-carcinoma sequence. Biomedicines 9(12):1937. https:\/\/doi.org\/10.3390\/biomedicines9121937","journal-title":"Biomedicines"},{"key":"533_CR41","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.3389\/fgene.2019.01077","volume":"10","author":"W Li","year":"2019","unstructured":"Li W, Yin Y, Quan X, Zhang H (2019) Gene expression value prediction based on XGBoost algorithm. Front Genet 10:1077. https:\/\/doi.org\/10.3389\/fgene.2019.01077","journal-title":"Front Genet"},{"issue":"15","key":"533_CR42","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1093\/bioinformatics\/btm287","volume":"23","author":"JG Liao","year":"2007","unstructured":"Liao JG, Chin K-V (2007) Logistic regression for disease classification using microarray data: Model selection in a large p and small n case. Bioinformatics 23(15):1945\u20131951. https:\/\/doi.org\/10.1093\/bioinformatics\/btm287","journal-title":"Bioinformatics"},{"issue":"3","key":"533_CR43","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.3892\/ol.2020.11278","volume":"19","author":"X Liu","year":"2020","unstructured":"Liu X, Liu X, Qiao T, Chen W (2020) Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis. Oncol Lett 19(3):1881\u20131889. https:\/\/doi.org\/10.3892\/ol.2020.11278","journal-title":"Oncol Lett"},{"issue":"2","key":"533_CR44","doi-asserted-by":"publisher","first-page":"124","DOI":"10.4251\/wjgo.v12.i2.124","volume":"12","author":"A Loktionov","year":"2020","unstructured":"Loktionov A (2020) Biomarkers for detecting colorectal cancer non-invasively: DNA, RNA or proteins? World J Gastroint Oncol 12(2):124\u2013148. https:\/\/doi.org\/10.4251\/wjgo.v12.i2.124","journal-title":"World J Gastroint Oncol"},{"issue":"15","key":"533_CR45","doi-asserted-by":"publisher","first-page":"4838","DOI":"10.7150\/jca.95622","volume":"15","author":"C Lu","year":"2024","unstructured":"Lu C, Chen S, Liu S, Liu H, Sun L, Sun Y (2024) Determination of the immunomodulatory role of OTOP2 in colon adenocarcinoma. J Cancer 15(15):4838\u20134852. https:\/\/doi.org\/10.7150\/jca.95622","journal-title":"J Cancer"},{"issue":"10","key":"533_CR46","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.3390\/genes14101836","volume":"14","author":"NS Maurya","year":"2023","unstructured":"Maurya NS, Kushwaha S, Vetukuri RR, Mani A (2023) Unlocking the potential of the CA2, CA7, and ITM2C gene signatures for the early detection of colorectal cancer: a comprehensive analysis of RNA-Seq data by utilizing machine learning algorithms. Genes 14(10):1836. https:\/\/doi.org\/10.3390\/genes14101836","journal-title":"Genes"},{"issue":"8","key":"533_CR47","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.3390\/pr9081466","volume":"9","author":"AU Mazlan","year":"2021","unstructured":"Mazlan AU, Sahabudin NA, Remli MA, Ismail NSN, Mohamad MS, Nies HW, Abd Warif NB (2021) A review on recent progress in machine learning and deep learning methods for cancer classification on gene expression data. Processes 9(8):1466. https:\/\/doi.org\/10.3390\/pr9081466","journal-title":"Processes"},{"issue":"1","key":"533_CR48","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1186\/s12885-018-4337-2","volume":"18","author":"H Meng","year":"2018","unstructured":"Meng H, Li W, Boardman LA, Wang L (2018) Loss of ZG16 is associated with molecular and clinicopathological phenotypes of colorectal cancer. BMC Cancer 18(1):433. https:\/\/doi.org\/10.1186\/s12885-018-4337-2","journal-title":"BMC Cancer"},{"issue":"2","key":"533_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.tranon.2020.101003","volume":"14","author":"H Meng","year":"2021","unstructured":"Meng H, Ding Y, Liu E, Li W, Wang L (2021) ZG16 regulates PD-L1 expression and promotes local immunity in colon cancer. Translational Oncology 14(2):101003. https:\/\/doi.org\/10.1016\/j.tranon.2020.101003","journal-title":"Translational Oncology"},{"issue":"1","key":"533_CR50","doi-asserted-by":"publisher","first-page":"32","DOI":"10.30476\/acrr.2021.89642.1080","volume":"9","author":"D Mitra","year":"2021","unstructured":"Mitra D, Dey A, Biswas I, Das-Mohapatra PK (2021) Bioactive compounds as a potential inhibitor of colorectal cancer; an insilico study of Gallic acid and Pyrogallol. Iran J Colorect Res 9(1):32\u201339. https:\/\/doi.org\/10.30476\/acrr.2021.89642.1080","journal-title":"Iran J Colorect Res"},{"key":"533_CR51","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/978-1-59745-530-5_14","volume":"404","author":"TG Nick","year":"2007","unstructured":"Nick TG, Campbell KM (2007) Logistic regression. Methods Mol Biol 404:273\u2013301. https:\/\/doi.org\/10.1007\/978-1-59745-530-5_14","journal-title":"Methods Mol Biol"},{"issue":"1","key":"533_CR52","doi-asserted-by":"publisher","first-page":"29882","DOI":"10.1038\/s41598-024-76083-9","volume":"14","author":"B Ning","year":"2024","unstructured":"Ning B, Chi J, Meng Q, Jia B (2024) Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images. Sci Rep 14(1):29882. https:\/\/doi.org\/10.1038\/s41598-024-76083-9","journal-title":"Sci Rep"},{"issue":"12","key":"533_CR53","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565\u20131567. https:\/\/doi.org\/10.1038\/nbt1206-1565","journal-title":"Nat Biotechnol"},{"key":"533_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopha.2022.112691","volume":"147","author":"S Nomiri","year":"2022","unstructured":"Nomiri S, Hoshyar R, Chamani E, Rezaei Z, Salmani F, Larki P, Tavakoli T, Gholipour F, Tabrizi NJ, Derakhshani A, Santarpia M, Franchina T, Brunetti O, Silvestris N, Safarpour H (2022) Prediction and validation of GUCA2B as the hub-gene in colorectal cancer based on co-expression network analysis: In-silico and in-vivo study. Biomed Pharmacother 147:112691","journal-title":"Biomed Pharmacother"},{"issue":"1","key":"533_CR55","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/nar\/27.1.29","volume":"27","author":"H Ogata","year":"1999","unstructured":"Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: kyoto encyclopedia of genes and genomes. Nucl Acids Res 27(1):29\u201334. https:\/\/doi.org\/10.1093\/nar\/27.1.29","journal-title":"Nucl Acids Res"},{"key":"533_CR56","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"533_CR57","doi-asserted-by":"publisher","first-page":"1277265","DOI":"10.3389\/fonc.2023.1277265","volume":"13","author":"M Piroozkhah","year":"2023","unstructured":"Piroozkhah M, Aghajani A, Jalali P, Shahmoradi A, Piroozkhah M, Tadlili Y, Salehi Z (2023) Guanylate cyclase-C Signaling Axis as a theragnostic target in colorectal cancer: a systematic review of literature. Front Oncol 13:1277265. https:\/\/doi.org\/10.3389\/fonc.2023.1277265","journal-title":"Front Oncol"},{"key":"533_CR58","doi-asserted-by":"publisher","unstructured":"Potghan S, Rajamenakshi R, Bhise A (2018) Multi-layer perceptron based lung tumor classification. In: 2018 second international conference on electronics, communication and aerospace technology (ICECA), pp. 499\u2013502. https:\/\/doi.org\/10.1109\/ICECA.2018.8474864","DOI":"10.1109\/ICECA.2018.8474864"},{"issue":"3","key":"533_CR59","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.3390\/ijms24032137","volume":"24","author":"F Rebuzzi","year":"2023","unstructured":"Rebuzzi F, Ulivi P, Tedaldi G (2023) Genetic predisposition to colorectal cancer: how many and which genes to test? Int J Mol Sci 24(3):2137. https:\/\/doi.org\/10.3390\/ijms24032137","journal-title":"Int J Mol Sci"},{"key":"533_CR60","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i3140","author":"RD Riley","year":"2016","unstructured":"Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. https:\/\/doi.org\/10.1136\/bmj.i3140","journal-title":"BMJ"},{"issue":"7","key":"533_CR61","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. https:\/\/doi.org\/10.1093\/nar\/gkv007","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"533_CR62","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and Empirical Analysis of ReliefF and RReliefF. Mach Learn 53(1):23\u201369. https:\/\/doi.org\/10.1023\/A:1025667309714","journal-title":"Mach Learn"},{"issue":"9","key":"533_CR63","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.3390\/cancers13092025","volume":"13","author":"T Sawicki","year":"2021","unstructured":"Sawicki T, Ruszkowska M, Danielewicz A, Nied\u017awiedzka E, Ar\u0142ukowicz T, Przyby\u0142owicz KE (2021) A review of colorectal cancer in terms of epidemiology, risk factors, development. Sympt Diagnos Cancers 13(9):2025. https:\/\/doi.org\/10.3390\/cancers13092025","journal-title":"Sympt Diagnos Cancers"},{"issue":"11","key":"533_CR64","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u20132504. https:\/\/doi.org\/10.1101\/gr.1239303","journal-title":"Genome Res"},{"issue":"8","key":"533_CR65","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1038\/s41575-022-00612-y","volume":"19","author":"A Shaukat","year":"2022","unstructured":"Shaukat A, Levin TR (2022) Current and future colorectal cancer screening strategies. Nat Rev Gastroenterol Hepatol 19(8):521\u2013531. https:\/\/doi.org\/10.1038\/s41575-022-00612-y","journal-title":"Nat Rev Gastroenterol Hepatol"},{"issue":"3","key":"533_CR66","first-page":"517","volume":"13","author":"W Shih","year":"2005","unstructured":"Shih W, Chetty R, Tsao MS (2005) Expression profiling by microarrays in colorectal cancer (Review). Oncol Rep 13(3):517\u2013524","journal-title":"Oncol Rep"},{"key":"533_CR67","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/msb.2011.75","volume":"7","author":"F Sievers","year":"2011","unstructured":"Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, S\u00f6ding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539. https:\/\/doi.org\/10.1038\/msb.2011.75","journal-title":"Mol Syst Biol"},{"key":"533_CR68","doi-asserted-by":"publisher","first-page":"967","DOI":"10.2147\/CIA.S109285","volume":"11","author":"K Simon","year":"2016","unstructured":"Simon K (2016) Colorectal cancer development and advances in screening. Clin Interv Aging 11:967\u2013976. https:\/\/doi.org\/10.2147\/CIA.S109285","journal-title":"Clin Interv Aging"},{"issue":"1","key":"533_CR69","doi-asserted-by":"publisher","first-page":"12","DOI":"10.11613\/BM.2014.003","volume":"24","author":"S Sperandei","year":"2014","unstructured":"Sperandei S (2014) Understanding logistic regression analysis. Biochemia Medica 24(1):12\u201318","journal-title":"Biochemia Medica"},{"issue":"1","key":"533_CR70","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/1756-0381-5-20","volume":"5","author":"ME Stokes","year":"2012","unstructured":"Stokes ME, Visweswaran S (2012) Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease. BioData Mining 5(1):20. https:\/\/doi.org\/10.1186\/1756-0381-5-20","journal-title":"BioData Mining"},{"key":"533_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105409","volume":"145","author":"Y Su","year":"2022","unstructured":"Su Y, Tian X, Gao R, Guo W, Chen C, Chen C, Jia D, Li H, Lv X (2022) Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Comput Biol Med 145:105409. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105409","journal-title":"Comput Biol Med"},{"issue":"D1","key":"533_CR72","doi-asserted-by":"publisher","first-page":"D638","DOI":"10.1093\/nar\/gkac1000","volume":"51","author":"D Szklarczyk","year":"2023","unstructured":"Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51(D1):D638\u2013D646. https:\/\/doi.org\/10.1093\/nar\/gkac1000","journal-title":"Nucleic Acids Res"},{"key":"533_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2023.102119","volume":"10","author":"D Tarwidi","year":"2023","unstructured":"Tarwidi D, Pudjaprasetya SR, Adytia D, Apri M (2023) An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX 10:102119. https:\/\/doi.org\/10.1016\/j.mex.2023.102119","journal-title":"MethodsX"},{"issue":"D1","key":"533_CR74","doi-asserted-by":"publisher","first-page":"D330","DOI":"10.1093\/nar\/gky1055","volume":"47","author":"The Gene Ontology Consortium","year":"2019","unstructured":"The Gene Ontology Consortium (2019) The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res 47(D1):D330\u2013D338. https:\/\/doi.org\/10.1093\/nar\/gky1055","journal-title":"Nucleic Acids Res"},{"key":"533_CR75","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: Introduction and review. J Biomed Inform 85:189\u2013203. https:\/\/doi.org\/10.1016\/j.jbi.2018.07.014","journal-title":"J Biomed Inform"},{"issue":"5","key":"533_CR76","doi-asserted-by":"publisher","first-page":"bbac191","DOI":"10.1093\/bib\/bbac191","volume":"23","author":"S Vadapalli","year":"2022","unstructured":"Vadapalli S, Abdelhalim H, Zeeshan S, Ahmed Z (2022) Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief Bioinform 23(5):bbac191. https:\/\/doi.org\/10.1093\/bib\/bbac191","journal-title":"Brief Bioinform"},{"key":"533_CR77","doi-asserted-by":"publisher","first-page":"735","DOI":"10.2147\/PGPM.S275941","volume":"13","author":"W Wang","year":"2020","unstructured":"Wang W, Sun JF, Wang XZ, Ying HQ, You XH, Sun F (2020) A novel prognostic score based on ZG16 for predicting CRC survival. Pharmacogenom Personal Med 13:735\u2013747. https:\/\/doi.org\/10.2147\/PGPM.S275941","journal-title":"Pharmacogenom Personal Med"},{"key":"533_CR78","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.apm.2021.12.016","volume":"105","author":"Y Wang","year":"2022","unstructured":"Wang Y, Zhang W, Fan M, Ge Q, Qiao B, Zuo X, Jiang B (2022) Regression with adaptive lasso and correlation based penalty. Appl Math Model 105:179\u2013196. https:\/\/doi.org\/10.1016\/j.apm.2021.12.016","journal-title":"Appl Math Model"},{"key":"533_CR79","doi-asserted-by":"publisher","DOI":"10.55299\/jostec.v4i1.251","author":"MR Wayahdi","year":"2022","unstructured":"Wayahdi MR, Ruziq F (2022) KNN and XGBoost algorithms for lung cancer prediction. J Sci Technol (JoSTec). https:\/\/doi.org\/10.55299\/jostec.v4i1.251","journal-title":"J Sci Technol (JoSTec)"},{"key":"533_CR80","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.573295","volume":"10","author":"FZ Wei","year":"2020","unstructured":"Wei FZ, Mei SW, Wang ZJ, Chen JN, Shen HY, Zhao FQ, Li J, Liu Z, Liu Q (2020) Differential expression analysis revealing CLCA1 to be a prognostic and diagnostic biomarker for colorectal cancer. Front Oncol 10:573295. https:\/\/doi.org\/10.3389\/fonc.2020.573295","journal-title":"Front Oncol"},{"issue":"13","key":"533_CR81","doi-asserted-by":"publisher","first-page":"11133","DOI":"10.3390\/ijms241311133","volume":"24","author":"W Wei","year":"2023","unstructured":"Wei W, Li Y, Huang T (2023) Using machine learning methods to study colorectal cancer tumor micro-environment and its biomarkers. Int J Mol Sci 24(13):11133. https:\/\/doi.org\/10.3390\/ijms241311133","journal-title":"Int J Mol Sci"},{"issue":"1","key":"533_CR82","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1080\/10485252.2010.490584","volume":"23","author":"Y Wu","year":"2011","unstructured":"Wu Y (2011) An ordinary differential equation based solution path algorithm. J Nonparam Stat 23(1):185\u2013199. https:\/\/doi.org\/10.1080\/10485252.2010.490584","journal-title":"J Nonparam Stat"},{"issue":"6","key":"533_CR83","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1002\/cam4.1484","volume":"7","author":"Z Wu","year":"2018","unstructured":"Wu Z, Liu Z, Ge W, Shou J, You L, Pan H, Han W (2018a) Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence. Cancer Med 7(6):2555\u20132566. https:\/\/doi.org\/10.1002\/cam4.1484","journal-title":"Cancer Med"},{"issue":"2","key":"533_CR84","first-page":"266","volume":"8","author":"Q Wu","year":"2018","unstructured":"Wu Q, Yang ZF, Wang KJ, Feng XY, Lv ZJ, Li Y, Jian ZX (2018b) AQP8 inhibits colorectal cancer growth and metastasis by down-regulating PI3K\/AKT signaling and PCDH7 expression. Am J Cancer Res 8(2):266\u2013279","journal-title":"Am J Cancer Res"},{"issue":"3","key":"533_CR85","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.3892\/or.2012.1891","volume":"28","author":"Z Yan","year":"2012","unstructured":"Yan Z, Li J, Xiong Y, Xu W, Zheng G (2012) Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data. Oncol Rep 28(3):1036\u20131042. https:\/\/doi.org\/10.3892\/or.2012.1891","journal-title":"Oncol Rep"},{"issue":"2","key":"533_CR86","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.3892\/mmr.2019.10336","volume":"20","author":"C Yu","year":"2019","unstructured":"Yu C, Chen F, Jiang J, Zhang H, Zhou M (2019) Screening key genes and signaling pathways in colorectal cancer by integrated bioinformatics analysis. Mol Med Rep 20(2):1259\u20131269. https:\/\/doi.org\/10.3892\/mmr.2019.10336","journal-title":"Mol Med Rep"},{"issue":"Suppl 2","key":"533_CR87","doi-asserted-by":"publisher","first-page":"S27","DOI":"10.1186\/1471-2164-9-S2-S27F","volume":"9","author":"Y Zhang","year":"2008","unstructured":"Zhang Y, Ding C, Li T (2008) Gene selection algorithm by combining reliefF and mRMR. BMC Genomics 9(Suppl 2):S27. https:\/\/doi.org\/10.1186\/1471-2164-9-S2-S27F","journal-title":"BMC Genomics"},{"key":"533_CR88","doi-asserted-by":"crossref","unstructured":"Zhang X, Jonassen I, Goks\u00f8yr A (2021) Machine learning approaches for biomarker discovery using gene expression data. In Helder N (ed) PubMed; Exon Publications. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK569564\/","DOI":"10.36255\/exonpublications.bioinformatics.2021.ch4"},{"issue":"5","key":"533_CR89","doi-asserted-by":"publisher","first-page":"3453","DOI":"10.21037\/tcr-19-2290","volume":"9","author":"ZG Zheng","year":"2020","unstructured":"Zheng ZG, Ma BQ, Xiao Y, Wang TX, Yu T, Huo YH, Wang QQ, Shan MJ, Meng LB, Han J (2020) Identification of biomarkers for the diagnosis and treatment of primary colorectal cancer based on microarray technology. Transl Cancer Res 9(5):3453\u20133467. https:\/\/doi.org\/10.21037\/tcr-19-2290","journal-title":"Transl Cancer Res"},{"issue":"1","key":"533_CR90","doi-asserted-by":"publisher","first-page":"17679","DOI":"10.1038\/s41598-024-68706-y","volume":"14","author":"S Zheng","year":"2024","unstructured":"Zheng S, He H, Zheng J, Zhu X, Lin N, Wu Q, Wei E, Weng C, Chen S, Huang X, Jian C, Guan S, Yang C (2024) Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer. Sci Rep 14(1):17679. https:\/\/doi.org\/10.1038\/s41598-024-68706-y","journal-title":"Sci Rep"},{"issue":"3","key":"533_CR91","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3892\/ijmm.2018.3359","volume":"41","author":"J Zhi","year":"2018","unstructured":"Zhi J, Sun J, Wang Z, Ding W (2018) Support vector machine classifier for prediction of the metastasis of colorectal cancer. Int J Mol Med 41(3):1419\u20131426. https:\/\/doi.org\/10.3892\/ijmm.2018.3359","journal-title":"Int J Mol Med"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00533-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-025-00533-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-025-00533-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T09:02:30Z","timestamp":1748941350000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-025-00533-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":91,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["533"],"URL":"https:\/\/doi.org\/10.1007\/s13721-025-00533-1","relation":{},"ISSN":["2192-6670"],"issn-type":[{"type":"electronic","value":"2192-6670"}],"subject":[],"published":{"date-parts":[[2025,6,2]]},"assertion":[{"value":"24 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data used in this study are collected from GEO (GSE44076).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}],"article-number":"40"}}