ArticleScientific reports2017
Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Scrub typhus-associated movement and gait disorders: A systematic review with principal component analysis and in silico mechanistic modelling.Tropical medicine & international health : TM & IH · 2025Pooled it
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- PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients.Scientific reports · 2021Article
- An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma.iScience · 2020Article
- Discovery of Ganoderma lucidum triterpenoids as potential inhibitors against Dengue virus NS2B-NS3 protease.Scientific reports · 2019Article
- Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients.Scientific reports · 2019Article
- Functional random forest with applications in dose-response predictions.Scientific reports · 2019Article
- Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC bioinformatics · 2019Article
- Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis.Frontiers in genetics · 2019Article
- Article
- Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes.BMC bioinformatics · 2018Article
- Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2018Article
- Sequential feature selection and inference using multi-variate random forests.Bioinformatics (Oxford, England) · 2018Article
- Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets.Scientific reports · 2017Article
- Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing.PloS one · 2017Article
- Extraction of robust functional connectivity patterns across psychiatric disorders using principal component analysis-based feature selection.Imaging neuroscience (Cambridge, Mass.)Article
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1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Dengue haemorrhagic fever (DHF) sometimes occurs after recovery from the disease caused by Dengue virus (DENV), and is often fatal. However, the mechanism of DHF has not been determined, possibly because no suitable methodologies are available to analyse this disease. Therefore, more innovative methods are required to analyse the gene expression profiles of DENV-infected patients. Principal components analysis (PCA)-based unsupervised feature extraction (FE) was applied to the gene expression profiles of DENV-infected patients, and an integrated analysis of two independent data sets identified 46 genes as critical for DHF progression. PCA using only these 46 genes rendered the two data sets highly consistent. The application of PCA to the 46 genes of an independent third data set successfully predicted the progression of DHF. A fourth in vitro data set confirmed the identification of the 46 genes. These 46 genes included interferon- and heme-biosynthesis-related genes. The former are enriched in binding sites for STAT1, STAT2, and IRF1, which are associated with DHF-promoting antibody-dependent enhancement, whereas the latter are considered to be related to the dysfunction of spliceosomes, which may mediate haemorrhage. These results are outcomes that other type of bioinformatic analysis could hardly achieve.
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