{"title":"可解释的人工智能进化抗菌肽","authors":"Jeremie Alexander, Gary Liu, Jonathan M. Stokes","doi":"10.1038/s41564-024-01919-z","DOIUrl":null,"url":null,"abstract":"An explainable deep learning model enables the identification and virtual optimization of antimicrobial peptides (AMPs) from the oral microbiome, yielding novel AMPs with activity against ESKAPE pathogens and demonstrating efficacy in a mouse wound infection model.","PeriodicalId":18992,"journal":{"name":"Nature Microbiology","volume":"10 2","pages":"267-269"},"PeriodicalIF":19.4000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Explainable artificial intelligence evolves antimicrobial peptides\",\"authors\":\"Jeremie Alexander, Gary Liu, Jonathan M. Stokes\",\"doi\":\"10.1038/s41564-024-01919-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An explainable deep learning model enables the identification and virtual optimization of antimicrobial peptides (AMPs) from the oral microbiome, yielding novel AMPs with activity against ESKAPE pathogens and demonstrating efficacy in a mouse wound infection model.\",\"PeriodicalId\":18992,\"journal\":{\"name\":\"Nature Microbiology\",\"volume\":\"10 2\",\"pages\":\"267-269\"},\"PeriodicalIF\":19.4000,\"publicationDate\":\"2025-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Microbiology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41564-024-01919-z\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Microbiology","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41564-024-01919-z","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
An explainable deep learning model enables the identification and virtual optimization of antimicrobial peptides (AMPs) from the oral microbiome, yielding novel AMPs with activity against ESKAPE pathogens and demonstrating efficacy in a mouse wound infection model.
期刊介绍：
Nature Microbiology aims to cover a comprehensive range of topics related to microorganisms. This includes:
Evolution: The journal is interested in exploring the evolutionary aspects of microorganisms. This may include research on their genetic diversity, adaptation, and speciation over time.
Physiology and cell biology: Nature Microbiology seeks to understand the functions and characteristics of microorganisms at the cellular and physiological levels. This may involve studying their metabolism, growth patterns, and cellular processes.
Interactions: The journal focuses on the interactions microorganisms have with each other, as well as their interactions with hosts or the environment. This encompasses investigations into microbial communities, symbiotic relationships, and microbial responses to different environments.
Societal significance: Nature Microbiology recognizes the societal impact of microorganisms and welcomes studies that explore their practical applications. This may include research on microbial diseases, biotechnology, or environmental remediation.
In summary, Nature Microbiology is interested in research related to the evolution, physiology and cell biology of microorganisms, their interactions, and their societal relevance.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
