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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Y-h. Taguchi

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease

Nächstes Kapitel Calculating Kolmogorov Complexity from the Transcriptome Data

https://ntp.niehs.nih.gov/drugmatrix/index.html.

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Titel: 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
verfasst von: Y-h. Taguchi
Verlag: Springer International Publishing
Buch: Intelligent Computing Theories and Application
Print ISBN: 978-3-319-63311-4

Electronic ISBN: 978-3-319-63312-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63312-1_45

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