{"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/machine-learning-a-virus-assembly-fitness","title":"Machine-learning a virus assembly fitness landscape","arxiv_id":"1901.05051","date":"2019-01-13","proceeding":null,"authors":["Pierre-Philippe Dechant","Yang-Hui He"],"abstract":"Realistic evolutionary fitness landscapes are notoriously difficult to\nconstruct. A recent cutting-edge model of virus assembly consists of a\ndodecahedral capsid with $12$ corresponding packaging signals in three affinity\nbands. This whole genome/phenotype space consisting of $3^{12}$ genomes has\nbeen explored via computationally expensive stochastic assembly models, giving\na fitness landscape in terms of the assembly efficiency. Using latest\nmachine-learning techniques by establishing a neural network, we show that the\nintensive computation can be short-circuited in a matter of minutes to\nastounding accuracy.","url_abs":"http://arxiv.org/abs/1901.05051v1","url_pdf":"http://arxiv.org/pdf/1901.05051v1.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":"machine-learning-a-virus-assembly-fitness","repo_url":"https://github.com/ppd22/ml_virus","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}