{"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/deepgo-predicting-protein-functions-from","title":"DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier","arxiv_id":"1705.05919","date":"2017-05-15","proceeding":null,"authors":["Maxat Kulmanov","Mohammed Asif Khan","Robert Hoehndorf"],"abstract":"A large number of protein sequences are becoming available through the\napplication of novel high-throughput sequencing technologies. Experimental\nfunctional characterization of these proteins is time-consuming and expensive,\nand is often only done rigorously for few selected model organisms.\nComputational function prediction approaches have been suggested to fill this\ngap. The functions of proteins are classified using the Gene Ontology (GO),\nwhich contains over 40,000 classes. Additionally, proteins have multiple\nfunctions, making function prediction a large-scale, multi-class, multi-label\nproblem.\n  We have developed a novel method to predict protein function from sequence.\nWe use deep learning to learn features from protein sequences as well as a\ncross-species protein-protein interaction network. Our approach specifically\noutputs information in the structure of the GO and utilizes the dependencies\nbetween GO classes as background information to construct a deep learning\nmodel. We evaluate our method using the standards established by the\nComputational Assessment of Function Annotation (CAFA) and demonstrate a\nsignificant improvement over baseline methods such as BLAST, with significant\nimprovement for predicting cellular locations.","url_abs":"http://arxiv.org/abs/1705.05919v1","url_pdf":"http://arxiv.org/pdf/1705.05919v1.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":"deepgo-predicting-protein-functions-from","repo_url":"https://github.com/bio-ontology-research-group/deepgo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.05919","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}