{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lu W"],"funding":["Fundamental Research Funds for the Central Universities","National Key R&D Program of China","National Key R&D Program of China","Opening Research Fund from Shanghai Key Laboratory of Stomatology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine","Zhejiang Provincial Natural Science Foundation of China","National Natural Science Foundation of China","Opening Research Fund from Shanghai Key Laboratory of Stomatology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine"],"pagination":["bbae373"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11285185"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(5)"],"pubmed_abstract":["Histone modifications, known as histone marks, are pivotal in regulating gene expression within cells. The vast array of potential combinations of histone marks presents a considerable challenge in decoding the regulatory mechanisms solely through biological experimental approaches. To overcome this challenge, we have developed a method called CatLearning. It utilizes a modified convolutional neural network architecture with a specialized adaptation Residual Network to quantitatively interpret histone marks and predict gene expression. This architecture integrates long-range histone information up to 500Kb and learns chromatin interaction features without 3D information. By using only one histone mark, CatLearning achieves a high level of accuracy. Furthermore, CatLearning predicts gene ex"],"journal":["Briefings in bioinformatics"],"pubmed_title":["CatLearning: highly accurate gene expression prediction from histone mark."],"pmcid":["PMC11285185"],"funding_grant_id":["2022YFA1302800","LZ24C060001","2019QN81005","32361133547, 32370613, 32222017, and 81874153","2022SKLS-KFKT002"],"pubmed_authors":["Liu Y","Shuai Q","Lu W","Lin S","Fang D","Zhang R","Tang Y","Cheng Y","Liang B"],"additional_accession":[]},"is_claimable":false,"name":"CatLearning: highly accurate gene expression prediction from histone mark.","description":"Histone modifications, known as histone marks, are pivotal in regulating gene expression within cells. The vast array of potential combinations of histone marks presents a considerable challenge in decoding the regulatory mechanisms solely through biological experimental approaches. To overcome this challenge, we have developed a method called CatLearning. It utilizes a modified convolutional neural network architecture with a specialized adaptation Residual Network to quantitatively interpret histone marks and predict gene expression. This architecture integrates long-range histone information up to 500Kb and learns chromatin interaction features without 3D information. By using only one histone mark, CatLearning achieves a high level of accuracy. Furthermore, CatLearning predicts gene ex","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-06-01T06:55:38.002Z","creation":"2026-04-08T10:20:10.713Z"},"accession":"S-EPMC11285185","cross_references":{"pubmed":["39073831"],"doi":["10.1093/bib/bbae373"]}}