ArticleGenomics, proteomics & bioinformatics2017
Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.
Article in Genomics, proteomics & bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- A bioinformatic single-cell and structure-informed framework identifies a baicalin-CA2-keratinocyte state axis in atopic dermatitis.PloS one · 2026Article
- Discriminative biomarker selection using hybrid multi-population evolutionary computation.Scientific reports · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- Identification of CSRP1 as novel biomarker for hormone-sensitive prostate cancer by the combination of clinical and functional research.Cancer cell international · 2025Article
- In-depth analysis of the RNA editing landscape in intracranial aneurysms and its potential role in alternative splicing.Computational and structural biotechnology journal · 2025Article
- Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study.BMC cancer · 2024Article
- Article
- A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization.BMC bioinformatics · 2023Article
- Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning.Scientific reports · 2023Article
- Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model.Frontiers in molecular biosciences · 2023Article
- CSRP1 expression is associated with a mesenchymal, stroma-rich tumor profile and poor prognosis in colon cancer.Turkish journal of medical sciences · 2023Article
- Benchmark study of feature selection strategies for multi-omics data.BMC bioinformatics · 2022Article
- GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints.Cancers · 2022Article
- Artificial Intelligence and Precision Medicine: A Perspective.Advances in experimental medicine and biology · 2022Article
- How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?International journal of environmental research and public health · 2021Article
- A novel gene selection method for gene expression data for the task of cancer type classification.Biology direct · 2021Article
- HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis.Frontiers in cell and developmental biology · 2021Article
- Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.Computational and mathematical methods in medicine · 2021Article
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4 authors.
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Abstract
It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.
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