ArticleJournal of medical systems2019
Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes.BMC bioinformatics · 2025Article
- Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory.Discover oncology · 2025Article
- The relationship between low-carbohydrate diet score, dietary macronutrient intake, and rheumatoid arthritis: results from NHANES 2011-2016.Clinical rheumatology · 2025Article
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- A novel and innovative cancer classification framework through a consecutive utilization of hybrid feature selection.BMC bioinformatics · 2023Article
- Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis.TheScientificWorldJournal · 2022Article
- Identification of Diagnostic Biomarkers Associated with Stromal and Immune Cell Infiltration in Fatty Infiltration After Rotator Cuff Tear by Integrating Bioinformatic Analysis and Machine-Learning.International journal of general medicine · 2022Article
- Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.Journal of healthcare engineering · 2022Article
- Cancer Detection and Prediction Using Genetic Algorithms.Computational intelligence and neuroscience · 2022Article
- Lung Cancer Stage Prediction Using Multi-Omics Data.Computational and mathematical methods in medicine · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- HARVESTMAN: a framework for hierarchical feature learning and selection from whole genome sequencing data.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification. In the first stage, Mutual Information based gene selection is applied which selects only the genes that have high information related to the cancer. The genes which have high mutual information value are given as input to the second stage. The Genetic Algorithm based gene selection is applied in the second stage to identify and select the optimal set of genes required for accurate classification. For classification, Support Vector Machine (SVM) is used. The proposed MI-GA gene selection approach is applied to Colon, Lung and Ovarian cancer datasets and the results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods.
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