{"title":"The decomposition of perturbation modeling","authors":"Stefan Peidli","doi":"10.1038/s43588-024-00706-4","DOIUrl":null,"url":null,"abstract":"A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"725-726"},"PeriodicalIF":12.0000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00706-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.