What is it about?
In order to identify drug compounds from gene expression analysis, combined methods using tensor decomposition was proposed.
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Why is it important?
By combining patients gene expression profiles and drug treated gene expression profiles, drug candidates compounds are inferred without training data set.
Perspectives
As a bioinformatics researcher, this is the first paper describing the extension from PCA based unsupervised FE to TD based unsupervised FE.
Professor Y-h. Taguchi
Chuo Daigaku
Read the Original
This page is a summary of: 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, January 2017, Springer Science + Business Media,
DOI: 10.1007/978-3-319-63312-1_45.
You can read the full text:
Resources
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
preprint version
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 Drug Matrix Datasets
presentation at ICIC2017 http://ic-ic.tongji.edu.cn/2017/index.htm
Contributors
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