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Identification of Candidate Drugs for Heart Failure Using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression Between Heart Failure and DrugMatrix Datasets

  • Y-h. TaguchiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

Abstract

Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets not mRNAs but proteins, mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data on gene expression during various drug treatments of rats were reported. I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported.

Keywords

Tensor decomposition Drug discovery Heart diseases 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of PhysicsChuo UniversityTokyoJapan

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