{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepchrome-deep-learning-for-predicting-gene","title":"DeepChrome: Deep-learning for predicting gene expression from histone modifications","arxiv_id":"1607.02078","date":"2016-07-07","proceeding":null,"authors":["Ritambhara Singh","Jack Lanchantin","Gabriel Robins","Yanjun Qi"],"abstract":"Motivation: Histone modifications are among the most important factors that\ncontrol gene regulation. Computational methods that predict gene expression\nfrom histone modification signals are highly desirable for understanding their\ncombinatorial effects in gene regulation. This knowledge can help in developing\n'epigenetic drugs' for diseases like cancer. Previous studies for quantifying\nthe relationship between histone modifications and gene expression levels\neither failed to capture combinatorial effects or relied on multiple methods\nthat separate predictions and combinatorial analysis. This paper develops a\nunified discriminative framework using a deep convolutional neural network to\nclassify gene expression using histone modification data as input. Our system,\ncalled DeepChrome, allows automatic extraction of complex interactions among\nimportant features. To simultaneously visualize the combinatorial interactions\namong histone modifications, we propose a novel optimization-based technique\nthat generates feature pattern maps from the learnt deep model. This provides\nan intuitive description of underlying epigenetic mechanisms that regulate\ngenes. Results: We show that DeepChrome outperforms state-of-the-art models\nlike Support Vector Machines and Random Forests for gene expression\nclassification task on 56 different cell-types from REMC database. The output\nof our visualization technique not only validates the previous observations but\nalso allows novel insights about combinatorial interactions among histone\nmodification marks, some of which have recently been observed by experimental\nstudies.","url_abs":"http://arxiv.org/abs/1607.02078v1","url_pdf":"http://arxiv.org/pdf/1607.02078v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepchrome-deep-learning-for-predicting-gene","repo_url":"https://github.com/QData/DeepChrome","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}