{"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/fast-pseudolikelihood-maximization-for-direct","title":"Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences","arxiv_id":"1401.4832","date":"2014-01-20","proceeding":null,"authors":["Magnus Ekeberg","Tuomo Hartonen","Erik Aurell"],"abstract":"Direct-Coupling Analysis is a group of methods to harvest information about\ncoevolving residues in a protein family by learning a generative model in an\nexponential family from data. In protein families of realistic size, this\nlearning can only be done approximately, and there is a trade-off between\ninference precision and computational speed. We here show that an earlier\nintroduced $l_2$-regularized pseudolikelihood maximization method called plmDCA\ncan be modified as to be easily parallelizable, as well as inherently faster on\na single processor, at negligible difference in accuracy. We test the new\nincarnation of the method on 148 protein families from the Protein Families\ndatabase (PFAM), one of the largest tests of this class of algorithms to date.","url_abs":"http://arxiv.org/abs/1401.4832v1","url_pdf":"http://arxiv.org/pdf/1401.4832v1.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":"fast-pseudolikelihood-maximization-for-direct","repo_url":"https://github.com/pagnani/PlmDCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}