{"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/ebic-an-evolutionary-based-parallel","title":"EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover","arxiv_id":"1801.03039","date":"2018-01-09","proceeding":null,"authors":["Patryk Orzechowski","Moshe Sipper","Xiuzhen Huang","Jason H. Moore"],"abstract":"In this paper a novel biclustering algorithm based on artificial intelligence\n(AI) is introduced. The method called EBIC aims to detect biologically\nmeaningful, order-preserving patterns in complex data. The proposed algorithm\nis probably the first one capable of discovering with accuracy exceeding 50%\nmultiple complex patterns in real gene expression datasets. It is also one of\nthe very few biclustering methods designed for parallel environments with\nmultiple graphics processing units (GPUs). We demonstrate that EBIC outperforms\nstate-of-the-art biclustering methods, in terms of recovery and relevance, on\nboth synthetic and genetic datasets. EBIC also yields results over 12 times\nfaster than the most accurate reference algorithms. The proposed algorithm is\nanticipated to be added to the repertoire of unsupervised machine learning\nalgorithms for the analysis of datasets, including those from large-scale\ngenomic studies.","url_abs":"http://arxiv.org/abs/1801.03039v2","url_pdf":"http://arxiv.org/pdf/1801.03039v2.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":"ebic-an-evolutionary-based-parallel","repo_url":"https://github.com/EpistasisLab/ebic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}