TransCell:In Silico Characterization of Genomic Landscape and Cellular Responses by Deep Transfer Learning  

作　　者：Shan-Ju Yeh Shreya Paithankar Ruoqiao Chen Jing Xing Mengying Sun Ke Liu Jiayu Zhou Bin Chen 

机构地区：[1]Department of Pediatrics and Human Development,Michigan State University,Grand Rapids,MI 49503,USA [2]Department of Pharmacology and Toxicology,Michigan State University,Grand Rapids,MI 49503,USA [3]Department of Computer Science and Engineering,Michigan State University,East Lansing,MI 48824,USA

出　　处：《Genomics, Proteomics & Bioinformatics》2024年第2期157-170,共14页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by the National Institute of Health(Grant Nos.R01GM134307 and K01 ES028047);the MSU Global Impact Initiative,USA.

摘　　要：Gene expression profiling of new or modified cell lines becomes routine today;however,obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines,including those derived from underrepresented groups,is not trivial when resources are minimal.Using gene expression to predict other measurements has been actively explored;however,systematic investigation of its predictive power in various measurements has not been well studied.Here,we evaluated commonly used machine learning methods and presented TransCell,a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses.Among these models,TransCell had the best performance in predicting metabolite,gene effect score(or genetic dependency),and drug sensitivity,and had comparable performance in predicting mutation,copy number variation,and protein expression.Notably,TransCell improved the performance by over 50%in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction.Furthermore,predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines,and predicted gene effect scores reflected BRAF resistance in melanoma cell lines.Together,we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal(http://apps.octad.org/transcell/)that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

关 键 词：GENOMICS TRANSCRIPTOMICS Cancer dependency Drug repurposing Transfer learning 

分 类 号：Q78[生物学—分子生物学]