{"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/deeptarget-end-to-end-learning-framework-for","title":"deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks","arxiv_id":"1603.09123","date":"2016-03-30","proceeding":null,"authors":["Byunghan Lee","Junghwan Baek","Seunghyun Park","Sungroh Yoon"],"abstract":"MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the\nexpression of target messenger RNAs (mRNAs) by binding them. Robust prediction\nof miRNA-mRNA pairs is of utmost importance in deciphering gene regulations but\nhas been challenging because of high false positive rates, despite a deluge of\ncomputational tools that normally require laborious manual feature extraction.\nThis paper presents an end-to-end machine learning framework for miRNA target\nprediction. Leveraged by deep recurrent neural networks-based auto-encoding and\nsequence-sequence interaction learning, our approach not only delivers an\nunprecedented level of accuracy but also eliminates the need for manual feature\nextraction. The performance gap between the proposed method and existing\nalternatives is substantial (over 25% increase in F-measure), and deepTarget\ndelivers a quantum leap in the long-standing challenge of robust miRNA target\nprediction.","url_abs":"http://arxiv.org/abs/1603.09123v2","url_pdf":"http://arxiv.org/pdf/1603.09123v2.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":"deeptarget-end-to-end-learning-framework-for","repo_url":"https://github.com/xinshuaiqi/awesome-genome","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}