{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:28Z","timestamp":1753875868322,"version":"3.41.2"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["SFB1002","KFO5002"],"award-info":[{"award-number":["SFB1002","KFO5002"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Histone modifications play an important role in transcription regulation. Although the general importance of some histone modifications for transcription regulation has been previously established, the relevance of others and their interaction is subject to ongoing research. By training Machine Learning models to predict a gene\u2019s expression and explaining their decision making process, we can get hints on how histone modifications affect transcription. In previous studies, trained models were either hardly explainable or the models were trained solely on the abundance of histone modifications. Based on other studies, which used histone modification patterns, rather than their abundance, to identify potential regulatory elements, we hypothesize the histone modification pattern in a gene\u2019s promoter to be more predictive for gene expression. We used an optimization algorithm to extract predictive histone modification profiles.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our algorithm called PatternChrome achieved an average area under curve (AUC) score of 0.9029 over 56 samples for binary classification, outperforming all previous algorithms for the same task. We explained the models decisions to deduce the effect of specific features, certain histone modifications or promoter positions on transcription regulation. Although the predictive histone modification patterns were extracted for each sample separately, they can be used to predict gene expression in other samples, implying that the created patterns are largely generalizable. Interestingly, the impact of histone modifications on gene regulation appears predominantly indifferent to cellular specificity. Through explanation of the classifier\u2019s decisions, we substantiate established literature knowledge while concurrently revealing novel insights into the intricate landscape of transcriptional regulation via histone modification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code for the PatternChrome algorithm, the scripts for the analyses and the required data can be found at (https:\/\/gitlab.gwdg.de\/MedBioinf\/generegulation\/patternchrome).<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf033","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T16:05:55Z","timestamp":1738166755000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5631-0126","authenticated-orcid":false,"given":"Niels Benjamin","family":"Paul","sequence":"first","affiliation":[{"name":"Department of Medical Bioinformatics, University Medical Center G\u00f6ttingen , G\u00f6ttingen 37099,","place":["Germany"]},{"name":"Clinic of Cardiology and Pneumology, University Medical Center G\u00f6ttingen , G\u00f6ttingen 37099,","place":["Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas Chanrithy","family":"Wolber","sequence":"additional","affiliation":[{"name":"Joint Research Center for Computational Biomedicine, RWTH Aachen University , Aachen 52074,","place":["Germany"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4549-903X","authenticated-orcid":false,"given":"Malte Lennart","family":"Sahrhage","sequence":"additional","affiliation":[{"name":"Department of Medical 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