ArticleNucleic acids research2013
iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 22 citations in OpenAlex.
- A Heuristic Machine Learning-Based Optimization Technique to Predict Lung Cancer Patient Survival.Computational intelligence and neuroscience · 2023Article
- A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.Scientific reports · 2020Article
- Parsimonious Gene Correlation Network Analysis (PGCNA): a tool to define modular gene co-expression for refined molecular stratification in cancer.NPJ systems biology and applications · 2019Article
- High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.Frontiers in genetics · 2019Article
- Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.BioMed research international · 2016Article
- Integrating heterogeneous genomic data to accurately identify disease subtypes.BMC medical genomics · 2015Article
- Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.Journal of translational medicine · 2015Article
- Machine learning applications in cancer prognosis and prediction.Computational and structural biotechnology journal · 2015Review
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4 authors at 1 institution in 1 country.
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Abstract
Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define 'correlation feature space' for samples based on the gene expression profiles by iterative employment of Pearson's correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.
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