{"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/deeperbind-enhancing-prediction-of-sequence","title":"DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding Proteins","arxiv_id":"1611.05777","date":"2016-11-17","proceeding":null,"authors":["Hamid Reza Hassanzadeh","May D. Wang"],"abstract":"Transcription factors (TFs) are macromolecules that bind to\n\\textit{cis}-regulatory specific sub-regions of DNA promoters and initiate\ntranscription. Finding the exact location of these binding sites (aka motifs)\nis important in a variety of domains such as drug design and development. To\naddress this need, several \\textit{in vivo} and \\textit{in vitro} techniques\nhave been developed so far that try to characterize and predict the binding\nspecificity of a protein to different DNA loci. The major problem with these\ntechniques is that they are not accurate enough in prediction of the binding\naffinity and characterization of the corresponding motifs. As a result,\ndownstream analysis is required to uncover the locations where proteins of\ninterest bind. Here, we propose DeeperBind, a long short term recurrent\nconvolutional network for prediction of protein binding specificities with\nrespect to DNA probes. DeeperBind can model the positional dynamics of probe\nsequences and hence reckons with the contributions made by individual\nsub-regions in DNA sequences, in an effective way. Moreover, it can be trained\nand tested on datasets containing varying-length sequences. We apply our\npipeline to the datasets derived from protein binding microarrays (PBMs), an\nin-vitro high-throughput technology for quantification of protein-DNA binding\npreferences, and present promising results. To the best of our knowledge, this\nis the most accurate pipeline that can predict binding specificities of DNA\nsequences from the data produced by high-throughput technologies through\nutilization of the power of deep learning for feature generation and positional\ndynamics modeling.","url_abs":"http://arxiv.org/abs/1611.05777v1","url_pdf":"http://arxiv.org/pdf/1611.05777v1.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":"deeperbind-enhancing-prediction-of-sequence","repo_url":"https://github.com/iwasakishuto/DeepScreening","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}