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

Genomics, Proteomics & Bioinformatics 2024年第2期22卷 157-170页

作者： Shan-Ju Yeh Shreya Paithankar Ruoqiao Chen Jing Xing Mengying Sun Ke Liu Jiayu Zhou Bin Chen Department of Pediatrics and Human Development Michigan State UniversityGrand RapidsMI 49503USA Department of Pharmacology and Toxicology Michigan State UniversityGrand RapidsMI 49503USA Department of Computer Science and Engineering Michigan State UniversityEast LansingMI 48824USA

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 *** gene expression to predict other measurements has been actively explored;however,systematic investigation of its predictive power in various measurements has not been well ***,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 *** 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 ***,TransCell improved the performance by over 50%in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score ***,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 ***,we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal(http://***/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

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