ArticlePloS one2017
Clustering cancer gene expression data by projective clustering ensemble.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Silencing APLNR enhances the radiosensitivity of prostate cancer by modulating the PI3K/AKT/mTOR signaling pathway.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- omicsGAT: Graph Attention Network for Cancer Subtype Analyses.International journal of molecular sciences · 2022Article
- Long non-coding RNA 00960 promoted the aggressiveness of lung adenocarcinoma via the miR-124a/SphK1 axis.Bioengineered · 2022Article
- Article
- Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study.PloS one · 2019Article
- Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps.BMC bioinformatics · 2018Article
- Eleven quick tips for running an interdisciplinary short course for new graduate students.PLoS computational biology · 2018Article
- Cancer Detection in Microarray Data Using a Modified Cat Swarm Optimization Clustering ApproachAsian Pacific journal of cancer prevention : APJCP · 2017Article
- Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis.BioMed research international · 2017Article
- Robust Feature Selection Approach for Patient Classification using Gene Expression Data.Bioinformation · 2017Article
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Authors and funding
3 authors.
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Abstract
Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data.
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