ArticleBMC genomics2017
A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.Briefings in bioinformatics · 2026Review
- Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.EBioMedicine · 2024Review
- Multicomponent comprehensive confirms that erythroferrone is a molecular biomarker of pan-cancer.Heliyon · 2024Article
- Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression.BMC bioinformatics · 2022Article
- The role of YTH domain containing 2 in epigenetic modification and immune infiltration of pan-cancer.Journal of cellular and molecular medicine · 2021Article
- Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model.Scientific reports · 2021Article
- MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.Clinical and translational medicine · 2021Article
- Multi-Omics Data Fusion via a Joint Kernel Learning Model for Cancer Subtype Discovery and Essential Gene Identification.Frontiers in genetics · 2021Article
- Identification of Claudin-6 as a Molecular Biomarker in Pan-Cancer Through Multiple Omics Integrative Analysis.Frontiers in cell and developmental biology · 2021Article
- Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.BMC genomics · 2020Article
- Highly robust model of transcription regulator activity predicts breast cancer overall survival.BMC medical genomics · 2020Article
- Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale.Frontiers in genetics · 2020Article
- A 3-MicroRNA Signature Identified From Serum Predicts Clinical Outcome of the Locally Advanced Gastric Cancer.Frontiers in oncology · 2020Article
- Manipulation of Magnetic Beads with Thin Film Microelectromagnet Traps.Micromachines · 2019Article
- The Potential Role of miRNAs as New Biomarkers for Osteoporosis.International journal of endocrinology · 2018Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria.
resultsThis study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster members are assessed as potential biomarkers. Both the criteria for the partition of clusters and selection of representative members are based on Fisher linear discriminant analysis (FDA). The FDA-based criterion has been demonstrated to be superior to three other criteria in selecting representative members, and also good at refining clusters. In the comparison with eight common feature selection methods, this clustering-based method performs the best with regard to the discriminative ability of selected biomarkers.
conclusionsOur experimental results demonstrate that the clustering-based method can identify microRNA combinatorial biomarkers with high accuracy and efficiency. Our method and data are available to the public upon request.
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