{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu Q"],"funding":["National Key R&D Program of China","NHGRI NIH HHS","National Natural Science Foundation of China","National Institutes of Health","Tsinghua-Fuzhou Institute for Data Technology","National Key Research and Development Program of China","Tsinghua-Fuzhou Institute"],"pagination":["496-507"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9801045"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(3)"],"pubmed_abstract":["Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin access"],"journal":["Genomics, proteomics & bioinformatics"],"pubmed_title":["DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility."],"pmcid":["PMC9801045"],"funding_grant_id":["R01 HG010359","61573207","P50 HG007735","RM1 HG007735","R01HG010359","61721003","61873141","P50HG007735","2018YFC0910404"],"pubmed_authors":["Hua K","Wong WH","Zhang X","Liu Q","Jiang R"],"additional_accession":[]},"is_claimable":false,"name":"DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.","description":"Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin access","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jun","modification":"2026-05-10T06:53:27.804Z","creation":"2025-02-19T03:26:40.656Z"},"accession":"S-EPMC9801045","cross_references":{"pubmed":["35293310"],"doi":["10.1016/j.gpb.2021.08.015"]}}