ArticleBMC medical genomics2019
A novel gene selection algorithm for cancer classification using microarray datasets.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.Frontiers in cardiovascular medicine · 2024Article
- FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.Genes · 2023Article
- A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis.International journal of molecular sciences · 2023Article
- Machine learning for pan-cancer classification based on RNA sequencing data.Frontiers in molecular biosciences · 2023Review
- An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies.Cancer informatics · 2023Review
- An Enhanced Hyper-Parameter Optimization of a Convolutional Neural Network Model for Leukemia Cancer Diagnosis in a Smart Healthcare System.Sensors (Basel, Switzerland) · 2022Article
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- A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis.TheScientificWorldJournal · 2022Article
- Quantitative Detection of Gastrointestinal Tumor Markers Using a Machine Learning Algorithm and Multicolor Quantum Dot Biosensor.Computational intelligence and neuroscience · 2022Article
- Identification of Feature Autophagy-Related Genes and DNA Methylation Profiles in Systemic Lupus Erythematosus Patients.Medical science monitor : international medical journal of experimental and clinical research · 2021Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- qPCR Analysis Reveals Association of Differential Expression ofFrontiers in endocrinology · 2021Article
- Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method.Biomolecules · 2020Article
- MicroRNA expression integrated analysis and identification of novel biomarkers in small cell lung cancer: a meta-analysis.Translational cancer research · 2020Article
- A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data.PeerJ. Computer science · 2020Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to the large number of genes involved. This fact is known as a curse of dimensionality, which is a challenging problem. Gene selection is a promising approach that addresses this problem and plays an important role in the development of efficient cancer classification due to the fact that only a small number of genes are related to the classification problem. Gene selection addresses many problems in microarray datasets such as reducing the number of irrelevant and noisy genes, and selecting the most related genes to improve the classification results.
methodsAn innovative Gene Selection Programming (GSP) method is proposed to select relevant genes for effective and efficient cancer classification. GSP is based on Gene Expression Programming (GEP) method with a new defined population initialization algorithm, a new fitness function definition, and improved mutation and recombination operators. . Support Vector Machine (SVM) with a linear kernel serves as a classifier of the GSP.
resultsExperimental results on ten microarray cancer datasets demonstrate that Gene Selection Programming (GSP) is effective and efficient in eliminating irrelevant and redundant genes/features from microarray datasets. The comprehensive evaluations and comparisons with other methods show that GSP gives a better compromise in terms of all three evaluation criteria, i.e., classification accuracy, number of selected genes, and computational cost. The gene set selected by GSP has shown its superior performances in cancer classification compared to those selected by the up-to-date representative gene selection methods.
conclusionGene subset selected by GSP can achieve a higher classification accuracy with less processing time.
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