Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm
Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm
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DOI：
10.1093/bioinformatics/btz313
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发表时间：
2019-07-15
期刊：
影响因子：
5.8
通讯作者：
Yamanishi, Yoshihiro
中科院分区：
文献类型：
--
作者：
Iwata, Michio;Yuan, Longhao;Yamanishi, Yoshihiro
人类细胞系对药物治疗的转录组反应的全基因组鉴定是医学和药学研究中的一个具有挑战性的问题。然而，药物诱导的基因表达谱在很大程度上是未知的和未观察到的所有组合的药物和人类细胞系，这是一个严重的障碍，在实际应用中。结果在这里，我们开发了一种新的计算方法来预测未知部分的药物诱导的基因表达谱的各种人类细胞系和预测新的药物治疗适应症，为广泛的疾病。提出了一种张量训练加权优化算法（TT-WOPT），用于预测张量结构基因表达数据中未知部分的潜在值。我们的研究结果表明，所提出的TT-WOPT算法可以准确地重建药物诱导的基因表达数据的一系列人类细胞系的综合网络为基础的细胞签名库。结果还表明，与使用原始基因表达谱相比，使用插补基因表达谱提高了药物重新定位的准确性。我们还对具有基因表达谱的疾病的药物适应症进行了全面的预测，这表明许多潜在的药物适应症是以前的方法无法预测的。
Motivation Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.Results Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.Supplementary informationSupplementary data are available at Bioinformatics online.