Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients.

Published on Mar 9, 2017in Scientific Reports4.38
路 DOI :10.1038/SREP44016
Y-h. Taguchi23
Estimated H-index: 23
(Chu-Dai: Chuo University)
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Abstract
Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients
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The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is dr...
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Abstract Publicly available gene expression profiles of the hippocampus measured during the successful administration of the histone deacetylase inhibitor, CI-994, to assist the extinction of mice contextual fear conditioning were re-analyzed using the recently proposed principal component analysis based unsupervised feature extraction. We identified 30 genes associated with differential gene expression in the hippocampus of mice treated with the HDAC inhibitor compared to controls; most of thes...
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#1Y-h. Taguchi (Chu-Dai: Chuo University)H-Index: 23
Background The recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to...
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MicroRNA(miRNA)鈥搈RNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of...
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