Mickael Durand , Sébastien Besseau , Nicolas Papon , Vincent Courdavault
{"title":"揭示植物生物活性途径：omics 数据利用和机器学习辅助","authors":"Mickael Durand , Sébastien Besseau , Nicolas Papon , Vincent Courdavault","doi":"10.1016/j.copbio.2024.103135","DOIUrl":null,"url":null,"abstract":"<div><p>Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.</p></div>","PeriodicalId":10833,"journal":{"name":"Current opinion in biotechnology","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0958166924000715/pdfft?md5=faeb460050cd7f736cb0e9555fd8e875&pid=1-s2.0-S0958166924000715-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting\",\"authors\":\"Mickael Durand , Sébastien Besseau , Nicolas Papon , Vincent Courdavault\",\"doi\":\"10.1016/j.copbio.2024.103135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.</p></div>\",\"PeriodicalId\":10833,\"journal\":{\"name\":\"Current opinion in biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0958166924000715/pdfft?md5=faeb460050cd7f736cb0e9555fd8e875&pid=1-s2.0-S0958166924000715-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current opinion in biotechnology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0958166924000715\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in biotechnology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0958166924000715","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Unlocking plant bioactive pathways: omics data harnessing and machine learning assisting
Plant bioactives hold immense potential in the medicine and food industry. The recent advancements in omics applied in deciphering specialized metabolic pathways underscore the importance of high-quality genome releases and the wealth of data in metabolomics and transcriptomics. While harnessing data, whether integrated or standalone, has proven successful in unveiling plant natural product (PNP) biosynthetic pathways, the democratization of machine learning in biology opens exciting new opportunities for enhancing the exploration of these pathways. This review highlights the recent breakthroughs in disrupting plant-specialized biosynthetic pathways through the utilization of omics data harnessing and machine learning techniques.
期刊介绍：
Current Opinion in Biotechnology (COBIOT) is renowned for publishing authoritative, comprehensive, and systematic reviews. By offering clear and readable syntheses of current advances in biotechnology, COBIOT assists specialists in staying updated on the latest developments in the field. Expert authors annotate the most noteworthy papers from the vast array of information available today, providing readers with valuable insights and saving them time.
As part of the Current Opinion and Research (CO+RE) suite of journals, COBIOT is accompanied by the open-access primary research journal, Current Research in Biotechnology (CRBIOT). Leveraging the editorial excellence, high impact, and global reach of the Current Opinion legacy, CO+RE journals ensure they are widely read resources integral to scientists' workflows.
COBIOT is organized into themed sections, each reviewed once a year. These themes cover various areas of biotechnology, including analytical biotechnology, plant biotechnology, food biotechnology, energy biotechnology, environmental biotechnology, systems biology, nanobiotechnology, tissue, cell, and pathway engineering, chemical biotechnology, and pharmaceutical biotechnology.
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