ArticleInternational journal of molecular sciences2016
Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction.
Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients.Scientific reports · 2021Article
- MiR-1254 andAmerican journal of translational research · 2021Article
- Article
- 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
- Influence of SIRT6 regulation of cellular glycometabolism on radiosensitivity of non-small-cell lung cancer A549 cells.International journal of clinical and experimental pathology · 2018Article
- Tensor decomposition-based unsupervised feature extraction identifies candidate genes that induce post-traumatic stress disorder-mediated heart diseases.BMC medical genomics · 2017Article
- 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
- Article
- Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients.Scientific reports · 2017Article
- Anti-NMDA Receptor Encephalitis and Vaccination.International journal of molecular sciences · 2017Article
- Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing.PloS one · 2017Article
- Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression.BioData mining · 2016Article
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Abstract
MicroRNA(miRNA)-mRNA 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 each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.
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