{"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/optimizing-scoring-function-of-dynamic","title":"Optimizing scoring function of dynamic programming of pairwise profile alignment using derivative free neural network","arxiv_id":"1708.09097","date":"2017-08-30","proceeding":null,"authors":["Kazunori D Yamada"],"abstract":"A profile comparison method with position-specific scoring matrix (PSSM) is\none of the most accurate alignment methods. Currently, cosine similarity and\ncorrelation coefficient are used as scoring functions of dynamic programming to\ncalculate similarity between PSSMs. However, it is unclear that these functions\nare optimal for profile alignment methods. At least, by definition, these\nfunctions cannot capture non-linear relationships between profiles. Therefore,\nin this study, we attempted to discover a novel scoring function, which was\nmore suitable for the profile comparison method than the existing ones. Firstly\nwe implemented a new derivative free neural network by combining the\nconventional neural network with evolutionary strategy optimization method.\nNext, using the framework, the scoring function was optimized for aligning\nremote sequence pairs. Nepal, the pairwise profile aligner with the novel\nscoring function significantly improved both alignment sensitivity and\nprecision, compared to aligners with the existing functions. Nepal improved\nalignment quality because of adaptation to remote sequence alignment and\nincreasing the expressive power of similarity score. The novel scoring function\ncan be realized using a simple matrix operation and easily incorporated into\nother aligners. With our scoring function, the performance of homology\ndetection and/or multiple sequence alignment for remote homologous sequences\nwould be further improved.","url_abs":"http://arxiv.org/abs/1708.09097v2","url_pdf":"http://arxiv.org/pdf/1708.09097v2.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":"optimizing-scoring-function-of-dynamic","repo_url":"https://github.com/yamada-kd/nepal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"multiple-sequence-alignment","task_name":"Multiple Sequence Alignment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}