{"title":"人工智能技术有助于了解表观转录组的分布。","authors":"Daiyun Huang, Jia Meng, Kunqi Chen","doi":"10.1016/j.xgen.2024.100718","DOIUrl":null,"url":null,"abstract":"<p><p>N<sup>6</sup>-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.<sup>1</sup> designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":"4 12","pages":"100718"},"PeriodicalIF":11.1000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI techniques have facilitated the understanding of epitranscriptome distribution.\",\"authors\":\"Daiyun Huang, Jia Meng, Kunqi Chen\",\"doi\":\"10.1016/j.xgen.2024.100718\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>N<sup>6</sup>-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.<sup>1</sup> designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\"4 12\",\"pages\":\"100718\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2024.100718\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2024.100718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
AI techniques have facilitated the understanding of epitranscriptome distribution.
N6-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al.1 designed a novel Transformer-BiGRU framework that achieves superior performance in computational m6A identification, thus demonstrating the potential of AI in genomic studies.
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