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

IF 11.5 2区生物学 Q1 GENETICS & HEREDITY Genomics, Proteomics & Bioinformatics Pub Date : 2024-01-10 DOI:10.1093/gpbjnl/qzad008

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

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Abstract 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. 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 has the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and has 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.

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TransCell：通过深度迁移学习对基因组图谱和细胞反应进行硅学表征

摘要如今，对新细胞系或改良细胞系进行基因表达谱分析已成为家常便饭；然而，在资源极度匮乏的情况下，要获得各种细胞系（包括来自代表性不足群体的细胞系）的全面分子特征和细胞反应并非易事。人们一直在积极探索利用基因表达来预测其他测量结果，但对其在各种测量结果中的预测能力的系统研究还不多。我们评估了常用的机器学习方法，并提出了 TransCell，这是一个两步深度迁移学习框架，利用从泛癌症肿瘤样本中获得的知识来预测分子特征和反应。在这些模型中，TransCell 在预测代谢物、基因效应得分（或遗传依赖性）和药物敏感性方面表现最佳，在预测突变、拷贝数变异和蛋白质表达方面表现相当。值得注意的是，TransCell 在药物敏感性预测方面的性能提高了 50%以上，在基因效应得分预测方面达到了 0.7 的相关性。此外，预测的药物敏感性揭示了新的 100 种儿科癌症细胞系的潜在再利用候选者，预测的基因效应得分反映了黑色素瘤细胞系中的 BRAF 抗药性。我们共同研究了基因表达在六种分子测量类型中的预测能力，并开发了一个门户网站（http://apps.octad.org/transcell/），该网站可完全通过基因表达谱预测 35.2 万个基因组和细胞反应特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry

CiteScore

14.30

自引率

4.20%

发文量

844

审稿时长

61 days

期刊介绍： Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.