{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T11:54:22Z","timestamp":1766577262462,"version":"3.48.0"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economic Development of the Russian Federation","award":["139-15-2025-006"],"award-info":[{"award-number":["139-15-2025-006"]}]},{"name":"Ministry of Economic Development of the Russian Federation","award":["000000C313925P3S0002"],"award-info":[{"award-number":["000000C313925P3S0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>In modern bioinformatics, the analysis of high-dimensional data (genomic, metabolomic, etc.) remains a critical challenge due to the \u201ccurse of dimensionality,\u201d where feature redundancy reduces classification efficiency and model interpretability. This study introduces a novel method, AIMarkerFinder (v0.1.0), for analyzing metabolomic data to identify key biomarkers. The method is based on a denoising autoencoder with an attention mechanism (DAE), enabling the extraction of informative features and the elimination of redundancy. Experiments on glioblastoma and adjacent tissue metabolomic data demonstrated that AIMarkerFinder reduces dimensionality from 446 to 4 key features while improving classification accuracy. Using the selected metabolites (Malonyl-CoA, Glycerophosphocholine, SM(d18:1\/22:0 OH), GC(18:1\/24:1)), the Random Forest and Kolmogorov\u2013Arnold Networks (KAN) models achieved accuracies of 0.904 and 0.937, respectively. The analytical formulas derived by the KAN provide model interpretability, which is critical for biomedical research. The proposed approach is applicable to genomics, transcriptomics, proteomics, and the study of exogenous factors on biological processes. The study\u2019s results open new prospects for personalized medicine and early disease diagnosis.<\/jats:p>","DOI":"10.3390\/informatics13010002","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T11:22:14Z","timestamp":1766575334000},"page":"2","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AIMarkerFinder: AI-Assisted Marker Discovery Based on an Integrated Approach of Autoencoders and Kolmogorov\u2013Arnold Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9433-8341","authenticated-orcid":false,"given":"Pavel S.","family":"Demenkov","sequence":"first","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, Pirogova Street 1, 630090 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-9155","authenticated-orcid":false,"given":"Timofey V.","family":"Ivanisenko","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, Pirogova Street 1, 630090 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1859-4631","authenticated-orcid":false,"given":"Vladimir A.","family":"Ivanisenko","sequence":"additional","affiliation":[{"name":"The Artificial Intelligence Research Center, Novosibirsk State University, Pirogova Street 1, 630090 Novosibirsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ygeno.2015.12.006","article-title":"Robust and Stable Gene Selection via Maximum\u2013Minimum Correntropy Criterion","volume":"107","author":"Mohammadi","year":"2016","journal-title":"Genomics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1093\/bioinformatics\/btu669","article-title":"GeneNet Toolbox for MATLAB: A Flexible Platform for the Analysis of Gene Connectivity in Biological Networks","volume":"31","author":"Taylor","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13087","DOI":"10.1038\/srep13087","article-title":"Machine Learning Methods for Quantitative Radiomic Biomarkers","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci. 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