{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:36:50Z","timestamp":1792787810286,"version":"4.5.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4t Ulm","id":[{"id":"https:\/\/ror.org\/032000t02","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The Gene Ontology (GO) is a public resource that describes gene functions and characteristics through a structured vocabulary of standardised terms. It currently contains annotations for over 1.5 million gene products, each linked to one or more GO terms. In this study, we propose integrating this GO-based semantic structure into machine learning systems for medical diagnostics. This approach serves a dual purpose: first, to prioritise genes that are semantically relevant to a given clinical task, thereby refining model input; and second, to enable the analysis of biologically predefined gene sets, which may reveal novel mechanisms underlying disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Evaluated across 16 benchmark data sets spanning diverse medical domains, our GO term-informed gene selection method generally outperformed models trained on full gene sets. Further analysis of individual GO terms not only enhanced classification performance but also identified high-performing, task-specific gene subsets that were overlooked during initial gene selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings demonstrate that Gene Ontology can be effectively leveraged for semantics-aware gene selection in clinical machine learning. Moreover, systematically evaluating individual GO terms offers a scalable strategy to uncover new, testable biological hypotheses\u2013revealing gene functions that might otherwise remain hidden when examining only broadly selected gene combinations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-026-06570-5","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T14:57:13Z","timestamp":1786460233000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic detection of predictive gene sets by semantics-based selection"],"prefix":"10.1186","volume":"27","author":[{"given":"Peter","family":"Eckhardt-Bellmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nahla A.","family":"Taha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silke D.","family":"Werle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johann M.","family":"Kraus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nensi","family":"Ikonomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-5254","authenticated-orcid":false,"given":"Hans A.","family":"Kestler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"6570_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01488-9","volume":"21","author":"S Secinaro","year":"2021","unstructured":"Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21:1\u201323.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"6570_CR2","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TAFFC.2019.2946774","volume":"13","author":"P Werner","year":"2022","unstructured":"Werner P, Martinez DL, Walter S, Al-Hamadi A, Gruss S, Picard RW. Automatic recognition methods supporting pain assessment: a survey. IEEE Trans Affect Comput. 2022;13(1):530\u201352.","journal-title":"IEEE Trans Affect Comput"},{"key":"6570_CR3","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s11547-020-01135-9","volume":"125","author":"E Neri","year":"2020","unstructured":"Neri E, Coppola F, Miele V, Bibbolino C, Grassi R. Artificial intelligence: Who is responsible for the diagnosis? Radiol Med (Torino). 2020;125:517\u201321.","journal-title":"Radiol Med (Torino)"},{"issue":"3","key":"6570_CR4","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s41060-018-0095-0","volume":"6","author":"JM Kraus","year":"2018","unstructured":"Kraus JM, Lausser L, Kuhn P, Jobst F, Bock M, Halanke C, et al. Big data and precision medicine: challenges and strategies with healthcare data. Int J Data Sci Anal. 2018;6(3):241\u20139.","journal-title":"Int J Data Sci Anal"},{"key":"6570_CR5","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157\u201382.","journal-title":"J Mach Learn Res"},{"key":"6570_CR6","volume-title":"Pattern recognition","author":"S Theodoridis","year":"1999","unstructured":"Theodoridis S, Koutroumbas K. Pattern recognition. San Diego, USA: Academic Press; 1999."},{"key":"6570_CR7","doi-asserted-by":"crossref","unstructured":"Liu H, Motoda H. Feature selection for knowledge discovery and data mining. The Springer international series in engineering and computer science, vol. 454. Springer, New York, NY 1998.","DOI":"10.1007\/978-1-4615-5689-3_3"},{"key":"6570_CR8","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.ins.2014.05.042","volume":"282","author":"V Bol\u00f3n-Canedo","year":"2014","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A, Ben\u00edtez JM, Herrera F. A review of microarray datasets and applied feature selection methods. Inf Sci. 2014;282:111\u201335.","journal-title":"Inf Sci"},{"issue":"2","key":"6570_CR9","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s11063-017-9706-3","volume":"48","author":"L Lausser","year":"2018","unstructured":"Lausser L, Szekely R, Schirra L, Kestler HA. The influence of multi-class feature selection on the prediction of diagnostic phenotypes. Neural Process Lett. 2018;48(2):863\u201380.","journal-title":"Neural Process Lett"},{"key":"6570_CR10","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s40537-019-0241-0","volume":"6","author":"K Tadist","year":"2019","unstructured":"Tadist K, Najah S, Nikolov NS, Mrabti F, Zahi A. Feature selection methods and genomic big data: a systematic review. J Big Data. 2019;6:79.","journal-title":"J Big Data"},{"issue":"5","key":"6570_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10142-024-01415-x","volume":"24","author":"K Borah","year":"2024","unstructured":"Borah K, Das HS, Seth S, Mallick K, Rahaman Z, Mallik S. A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis. Funct Integr Genomics. 2024;24(5):139.","journal-title":"Funct Integr Genomics"},{"key":"6570_CR12","doi-asserted-by":"publisher","unstructured":"Kestler, H.A., Simon, S., Baune, A., Hagenbuchner, M., Schwenker, F., Palm, G. (1999). A Hierarchical Neural Object Classifier for Subsymbolic-Symbolic Coupling. In: F\u00f6rstner, W., Buhmann, J.M., Faber, A., Faber, P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-60243-6_3","DOI":"10.1007\/978-3-642-60243-6_3"},{"issue":"4","key":"6570_CR13","doi-asserted-by":"publisher","first-page":"158","DOI":"10.3390\/biom8040158","volume":"8","author":"L Lausser","year":"2018","unstructured":"Lausser L, Siegle L, Rottbauer W, Frank D, Just S, Kestler HA. Semantic multi-classifier systems identify predictive processes in heart failure models across species. Biomolecules. 2018;8(4):158.","journal-title":"Biomolecules"},{"issue":"1","key":"6570_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/s24010037","volume":"24","author":"S Das","year":"2024","unstructured":"Das S, Dey MK, Devireddy R, Gartia MR. Biomarkers in cancer detection, diagnosis, and prognosis. Sensors. 2024;24(1):37.","journal-title":"Sensors"},{"issue":"1","key":"6570_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25\u20139.","journal-title":"Nat Genet"},{"key":"6570_CR16","doi-asserted-by":"crossref","unstructured":"Asif M, Martiniano HFMCM, Vicente AM, Couto FM. Identifying disease genes using machine learning and gene functional similarities, assessed through gene ontology. PLOS ONE 2018;13:0208626.","DOI":"10.1371\/journal.pone.0208626"},{"key":"6570_CR17","doi-asserted-by":"crossref","unstructured":"Vapnik VN. The nature of statistical learning theory. 2nd ed. Statistics for Engineering and Information Science. New York, NY: Springer; 2000.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"6570_CR18","unstructured":"Abe S. Support vector machines for pattern classification. Advances in pattern recognition. London, U.K.: Springer; 2005."},{"issue":"1","key":"6570_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"issue":"10","key":"6570_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1004467","volume":"11","author":"Y Pritykin","year":"2015","unstructured":"Pritykin Y, Ghersi D, Singh M. Genome-wide detection and analysis of multifunctional genes. PLoS Comput Biol. 2015;11(10):1\u201327.","journal-title":"PLoS Comput Biol"},{"issue":"7","key":"6570_CR21","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.tim.2009.05.001","volume":"17","author":"MC Chibucos","year":"2009","unstructured":"Chibucos MC, Tseng T-T, Setubal JC. Describing commonalities in microbial effector delivery using the gene ontology. Trends Microbiol. 2009;17(7):312\u20139.","journal-title":"Trends Microbiol"},{"key":"6570_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-1004-8","volume":"19","author":"S Uddin","year":"2019","unstructured":"Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak. 2019;19:1\u201316.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"6570_CR23","doi-asserted-by":"publisher","first-page":"9099","DOI":"10.1038\/s41598-022-13052-0","volume":"12","author":"SR Salarikia","year":"2022","unstructured":"Salarikia SR, Kashkooli M, Taghipour MJ, Malekpour M, Negahdaripour M. Identification of hub pathways and drug candidates in gastric cancer through systems biology. Sci Rep. 2022;12(1):9099.","journal-title":"Sci Rep"},{"issue":"13","key":"6570_CR24","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1093\/bioinformatics\/btm201","volume":"23","author":"WK Lim","year":"2007","unstructured":"Lim WK, Wang K, Lefebvre C, Califano A. Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics. 2007;23(13):282\u20138.","journal-title":"Bioinformatics"},{"issue":"6","key":"6570_CR25","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1093\/bib\/bbs046","volume":"14","author":"M Dillies","year":"2013","unstructured":"Dillies M, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings Bioinform. 2013;14(6):671\u201383.","journal-title":"Briefings Bioinform"},{"issue":"7493","key":"6570_CR26","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1038\/nature13163","volume":"507","author":"T Lu","year":"2014","unstructured":"Lu T, Aron L, Zullo J, Pan Y, Kim H, Chen Y, et al. Rest and stress resistance in ageing and Alzheimer\u2019s disease. Nature. 2014;507(7493):448\u201354.","journal-title":"Nature"},{"issue":"88","key":"6570_CR27","first-page":"2016","volume":"55","author":"L Badea","year":"2008","unstructured":"Badea L, Herlea V, Dima SO, Dumitrascu T, Popescu I, et al. Combined gene expression analysis of whole-tissue and microdissected pancreatic ductal adenocarcinoma identifies genes specifically overexpressed in tumor epithelia. Hepatogastroenterology. 2008;55(88):2016.","journal-title":"Hepatogastroenterology"},{"issue":"6","key":"6570_CR28","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.alcohol.2014.07.004","volume":"48","author":"JN McClintick","year":"2014","unstructured":"McClintick JN, Brooks AI, Deng L, Liang L, Wang JC, Kapoor M, et al. Ethanol treatment of lymphoblastoid cell lines from alcoholics and non-alcoholics causes many subtle changes in gene expression. Alcohol. 2014;48(6):603\u201310.","journal-title":"Alcohol"},{"key":"6570_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2407-5-45","volume":"5","author":"UR Chandran","year":"2005","unstructured":"Chandran UR, Dhir R, Ma C, Michalopoulos G, Becich M, Gilbertson J. Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors. BMC Cancer. 2005;5:1\u201311.","journal-title":"BMC Cancer"},{"issue":"1","key":"6570_CR30","doi-asserted-by":"publisher","first-page":"52198","DOI":"10.1371\/journal.pone.0052198","volume":"8","author":"CW Woods","year":"2013","unstructured":"Woods CW, McClain MT, Chen M, Zaas AK, Nicholson BP, Varkey J, et al. A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2. PLoS ONE. 2013;8(1):52198.","journal-title":"PLoS ONE"},{"issue":"5","key":"6570_CR31","first-page":"0302947","volume":"19","author":"T Chen","year":"2024","unstructured":"Chen T, Kabir MF. Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data. PLoS ONE. 2024;19(5):0302947.","journal-title":"PLoS ONE"},{"issue":"4","key":"6570_CR32","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1097\/HEP.0000000000001023","volume":"81","author":"RA Hlady","year":"2025","unstructured":"Hlady RA, Zhao X, El Khoury LY, Wagner RT, Luna A, Pham K, et al. Epigenetic heterogeneity hotspots in human liver disease progression. Hepatology. 2025;81(4):1197\u2013210.","journal-title":"Hepatology"},{"issue":"1","key":"6570_CR33","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s41698-023-00479-5","volume":"7","author":"O Krali","year":"2023","unstructured":"Krali O, Marincevic-Zuniga Y, Arvidsson G, Enblad AP, Lundmark A, Sayyab S, et al. Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia. NPJ Precis Oncol. 2023;7(1):131.","journal-title":"NPJ Precis Oncol"},{"issue":"1","key":"6570_CR34","doi-asserted-by":"publisher","first-page":"30349","DOI":"10.1038\/s41598-025-15532-5","volume":"15","author":"JB Baluku","year":"2025","unstructured":"Baluku JB, Namiiro S, Zawedde DK, Namanda B, Kawalya H, Najjingo I, et al. DNA methylation patterns associated with prior tuberculosis infection in people with HIV. Sci Rep. 2025;15(1):30349.","journal-title":"Sci Rep"},{"issue":"1","key":"6570_CR35","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s13073-024-01323-6","volume":"16","author":"S Ramakrishnan","year":"2024","unstructured":"Ramakrishnan S, Cortes-Gomez E, Athans SR, Attwood KM, Rosario SR, Kim SJ, et al. Race-specific coregulatory and transcriptomic profiles associated with DNA methylation and androgen receptor in prostate cancer. Genome Med. 2024;16(1):52.","journal-title":"Genome Med"},{"issue":"11","key":"6570_CR36","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1158\/2767-9764.CRC-23-0165","volume":"3","author":"W Tang","year":"2023","unstructured":"Tang W, Zhang F, Byun JS, Dorsey TH, Yfantis HG, Ajao A, et al. Population-specific mutation patterns in breast tumors from African American, European American, and Kenyan Patients. Cancer Res Commun. 2023;3(11):2244\u201355.","journal-title":"Cancer Res Commun"},{"issue":"5","key":"6570_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.gastha.2025.100621","volume":"4","author":"SO Antwi","year":"2025","unstructured":"Antwi SO, Siaw ADJ, Armasu SM, Frank JA, Yan IK, Ahmed FY, et al. Genome-wide DNA methylation markers associated with metabolic liver cancer. Gastro Hep Adv. 2025;4(5):100621.","journal-title":"Gastro Hep Adv"},{"issue":"1","key":"6570_CR38","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol. 1996;58(1):267\u201388.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"D1","key":"6570_CR39","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1093\/nar\/gky1055","volume":"47","author":"Gene Ontology Consortium","year":"2019","unstructured":"Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):330\u20138.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"6570_CR40","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/nar\/gkaa1113","volume":"49","author":"Gene Ontology Consortium","year":"2021","unstructured":"Gene Ontology Consortium. The gene ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021;49(D1):325\u201334.","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"6570_CR41","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1001\/archinte.1996.00440080047006","volume":"156","author":"NRC Campbell","year":"1996","unstructured":"Campbell NRC, Hull RD, Brant R, Hogan DB, Pineo G, Raskob GE. Aging and heparin-related bleeding. JAMA Intern Med. 1996;156(8):857\u201360.","journal-title":"JAMA Intern Med"},{"issue":"6","key":"6570_CR42","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1111\/j.1538-7836.2010.03847.x","volume":"8","author":"V Ignjatovic","year":"2010","unstructured":"Ignjatovic V, Straka E, Summerhayes R, Monagle P. Age-specific differences in binding of heparin to plasma proteins. J Thromb Haemost. 2010;8(6):1290\u20134.","journal-title":"J Thromb Haemost"},{"key":"6570_CR43","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s13024-024-00757-1","volume":"19","author":"Q Guo","year":"2024","unstructured":"Guo Q, Ping L, Dammer EB, Duong DM, Yin L, Xu K, et al. Heparin-enriched plasama proteome is significantly altered in Alzheimer\u2019s disease. Mol Neurodegener. 2024;19:67.","journal-title":"Mol Neurodegener"},{"key":"6570_CR44","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1111\/j.1755-5949.2012.00371.x","volume":"18","author":"A Oyagi","year":"2012","unstructured":"Oyagi A, Hara H. Essential roles of heparin-binding epidermal growth factor-like growth factor in the brain. CNS Neurosci Therap. 2012;18:803\u201310.","journal-title":"CNS Neurosci Therap"},{"key":"6570_CR45","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1038\/nature20603","volume":"540","author":"S Schw\u00f6rer","year":"2016","unstructured":"Schw\u00f6rer S, Becker F, Feller C, Baig AH, K\u00f6ber U, Henze H, et al. Epigenetic stress responses induce muscle stem-cell ageing by Hoxa9 developmental signals. Nature. 2016;540:428\u201332.","journal-title":"Nature"},{"issue":"8","key":"6570_CR46","doi-asserted-by":"publisher","first-page":"73","DOI":"10.3390\/cancers8080073","volume":"8","author":"J Gruber","year":"2016","unstructured":"Gruber J, Yee Z, Tolwinski NS. Developmental drift and the role of Wnt signaling in aging. Cancers. 2016;8(8):73.","journal-title":"Cancers"},{"issue":"3","key":"6570_CR47","doi-asserted-by":"publisher","first-page":"0195126","DOI":"10.1371\/journal.pone.0195126","volume":"13","author":"L Siegle","year":"2018","unstructured":"Siegle L, Schwab JD, K\u00fchlwein SD, Lausser L, T\u00fcmpel S, Pfister AS, et al. A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells. PLoS ONE. 2018;13(3):0195126.","journal-title":"PLoS ONE"},{"issue":"8","key":"6570_CR48","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1093\/bioinformatics\/bti169","volume":"21","author":"HA Kestler","year":"2005","unstructured":"Kestler HA, M\u00fcller A, Gress TM, Buchholz M. Generalized Venn diagrams: a new method of visualizing complex genetic set relations. Bioinform. 2005;21(8):1592\u20135.","journal-title":"Bioinform"},{"key":"6570_CR49","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/1471-2105-9-67","volume":"9","author":"HA Kestler","year":"2008","unstructured":"Kestler HA, M\u00fcller A, Kraus JM, Buchholz M, Gress TM, Liu H, et al. VennMaster: area-proportional Euler diagrams for functional GO analysis of microarrays. BMC Bioinform. 2008;9:67.","journal-title":"BMC Bioinform"},{"issue":"1","key":"6570_CR50","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41592-024-02525-x","volume":"22","author":"M Hu","year":"2025","unstructured":"Hu M, Alkhairy S, Lee I, Pillich RT, Fong D, Smith K, et al. Evaluation of large language models for discovery of gene set function. Nat Methods. 2025;22(1):82\u201391.","journal-title":"Nat Methods"},{"issue":"1","key":"6570_CR51","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27\u201330.","journal-title":"Nucleic Acids Res"},{"issue":"Suppl1","key":"6570_CR52","first-page":"428","volume":"33","author":"G Joshi-Tope","year":"2005","unstructured":"Joshi-Tope G, Gillespie M, Vastrik I, D\u2019Eustachio P, Schmidt E, Bono B, et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005;33(Suppl1):428\u201332.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"6570_CR53","doi-asserted-by":"publisher","first-page":"3591","DOI":"10.1038\/s41467-025-58439-5","volume":"16","author":"D Lu","year":"2025","unstructured":"Lu D, Zheng Y, Yi X, Hao J, Zeng X, Han L, et al. Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning. Nat Commun. 2025;16(1):3591.","journal-title":"Nat Commun"},{"issue":"3","key":"6570_CR54","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1093\/bib\/bbaf269","volume":"26","author":"A Stolnicu","year":"2025","unstructured":"Stolnicu A, Ikonomi N, Eckhardt-Bellmann P, Kraus JM, Kestler HA. Robust signalling entropy estimation for biological process characterisation. Brief Bioinform. 2025;26(3):269.","journal-title":"Brief Bioinform"},{"key":"6570_CR55","unstructured":"American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders: DSM-IV. Washington, DC; 1994."},{"key":"6570_CR56","unstructured":"American Psychiatric Association: diagnostic and statistical manual of mental disorders: DSM-III-R. (3rd edn) Washington, DC; 1987."},{"key":"6570_CR57","unstructured":"World Health Organization: International classification of disease. Geneva; 1993."},{"key":"6570_CR58","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1001\/archpsyc.1972.01750190059011","volume":"26","author":"JP Feighner","year":"1972","unstructured":"Feighner JP, Robins E, Guze SB, Woodruff RAJ, Winokur G, Munoz R. Diagnostic criteria for use in psychiatric research. Arch Gen Psychiatry. 1972;26:57\u201363.","journal-title":"Arch Gen Psychiatry"},{"issue":"16","key":"6570_CR59","doi-asserted-by":"publisher","first-page":"3439","DOI":"10.1093\/bioinformatics\/bti525","volume":"21","author":"S Durinck","year":"2005","unstructured":"Durinck S, Moreau Y, Kasprzyk A, Davis SR, Moor BD, Brazma A, et al. Biomart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinform. 2005;21(16):3439\u201340.","journal-title":"Bioinform"},{"issue":"8","key":"6570_CR60","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1038\/nprot.2009.97","volume":"4","author":"S Durinck","year":"2009","unstructured":"Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R\/bioconductor package biomart. Nat Protoc. 2009;4(8):1184\u201391.","journal-title":"Nat Protoc"},{"issue":"3","key":"6570_CR61","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. affy - analysis of affymetrix genechip data at the probe level. Bioinform. 2004;20(3):307\u201315.","journal-title":"Bioinform"},{"key":"6570_CR62","unstructured":"Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F. E1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. 2024. R package version 1.7-16. https:\/\/CRAN.R-project.org\/package=e1071."},{"issue":"3","key":"6570_CR63","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M. Classification and regression by random forest. R News. 2002;2(3):18\u201322.","journal-title":"R News"},{"issue":"1","key":"6570_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1\u201322.","journal-title":"J Stat Softw"},{"issue":"1","key":"6570_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v106.i01","volume":"106","author":"JK Tay","year":"2023","unstructured":"Tay JK, Narasimhan B, Hastie T. Elastic net regularization paths for all generalized linear models. J Stat Softw. 2023;106(1):1\u201331.","journal-title":"J Stat Softw"},{"key":"6570_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1\u201326.","journal-title":"J Stat Softw"},{"issue":"1","key":"6570_CR67","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13059-016-0953-9","volume":"17","author":"J Xin","year":"2016","unstructured":"Xin J, Mark A, Afrasiabi C, Tsueng G, Juchler M, Gopal N, et al. High-performance web services for querying gene and variant annotation. Genome Biol. 2016;17(1):91.","journal-title":"Genome Biol"},{"issue":"D1","key":"6570_CR68","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1093\/nar\/gks1114","volume":"41","author":"C Wu","year":"2013","unstructured":"Wu C, Macleod I, Su AI. BioGPS and MyGene.info: organizing online, gene-centric information. Nucleic Acids Res. 2013;41(D1):561\u20135.","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"6570_CR69","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1093\/bioinformatics\/btac017","volume":"38","author":"S Lelong","year":"2022","unstructured":"Lelong S, Zhou X, Afrasiabi C, Qian Z, Cano MA, Tsueng G, et al. BioThings SDK: a toolkit for building high-performance data APIs in biomedical research. Bioinformatics. 2022;38(7):2077\u20139.","journal-title":"Bioinformatics"},{"key":"6570_CR70","doi-asserted-by":"publisher","first-page":"10872","DOI":"10.1038\/s41598-018-28948-z","volume":"8","author":"DV Klopfenstein","year":"2018","unstructured":"Klopfenstein DV, Zhang L, Pedersen BS, Ram\u00edrez F, et al. GOATOOLS: a python library for gene ontology analyses. Sci Rep. 2018;8:10872.","journal-title":"Sci Rep"},{"key":"6570_CR71","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. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-026-06570-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06570-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06570-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:03:45Z","timestamp":1790082225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12859-026-06570-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6570"],"URL":"https:\/\/doi.org\/10.1186\/s12859-026-06570-5","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"assertion":[{"value":"8 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"221"}}