机器学习和基因组学在孤儿作物改良中的应用

IF 15.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Nature Communications Pub Date : 2025-01-24 DOI:10.1038/s41467-025-56330-x

Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry, David Edwards

{"title":"机器学习和基因组学在孤儿作物改良中的应用","authors":"Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry, David Edwards","doi":"10.1038/s41467-025-56330-x","DOIUrl":null,"url":null,"abstract":"<p>Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"52 1","pages":""},"PeriodicalIF":15.7000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of machine learning and genomics for orphan crop improvement\",\"authors\":\"Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry, David Edwards\",\"doi\":\"10.1038/s41467-025-56330-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.</p>\",\"PeriodicalId\":19066,\"journal\":{\"name\":\"Nature Communications\",\"volume\":\"52 1\",\"pages\":\"\"},\"PeriodicalIF\":15.7000,\"publicationDate\":\"2025-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Communications\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41467-025-56330-x\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-56330-x","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

孤儿作物是发展中地区重要的营养来源，许多作物对生物和非生物胁迫具有耐受性；然而，由于缺乏资源，现代作物改良技术尚未广泛应用于孤儿作物。在主要作物类型中都有孤儿作物的代表，这些相关物种之间的基因保存可以用于作物改良。机器学习（ML）已经成为作物改良的一个有前途的工具。将知识从主要作物转移到孤儿作物，并利用机器学习来提高准确性和效率，可以用来改善孤儿作物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Application of machine learning and genomics for orphan crop improvement

查看原文本刊更多论文

Application of machine learning and genomics for orphan crop improvement

Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Nature Communications Biological Science Disciplines-

CiteScore

24.90

自引率

2.40%

发文量

6928

审稿时长

3.7 months

期刊介绍： Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.