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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 expression by simulating changes in histone modifications at enhancers and throughout the genome. These findings help comprehend the architecture of histone marks and develop diagnostic and therapeutic targets for diseases with epigenetic changes.<\/jats:p>","DOI":"10.1093\/bib\/bbae373","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T23:33:03Z","timestamp":1722295983000},"source":"Crossref","is-referenced-by-count":3,"title":["CatLearning: highly accurate gene expression prediction from histone mark"],"prefix":"10.1093","volume":"25","author":[{"given":"Weining","family":"Lu","sequence":"first","affiliation":[{"name":"Tsinghua University Beijing National Research Center for Information Science and Technology, , FIT Building, Haidian District, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Tang","sequence":"additional","affiliation":[{"name":"Zhejiang University Liangzhu Laboratory, , 1369 Wenyixi Road, Yuhang District, Hangzhou, Zhejiang, 311121, 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