ArticleMedical & biological engineering & computing2022
Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
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- Identification of Prognostic Factors in Esophageal Cancer Using Machine Learning: A Retrospective Study Based on the SEER Database.Journal of clinical medicine · 2026Article
- New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.Journal of imaging informatics in medicine · 2026Article
- A multiobjective human evolutionary optimization algorithm for complex engineering problems.Scientific reports · 2026Article
- Predicting social isolation in maintenance hemodialysis patients using machine learning methods: a cross-sectional study.Frontiers in psychiatry · 2026Article
- Development and external validation of a machine learning model for predicting in-hospital mortality in ICU patients with diabetic kidney disease: a study utilizing the MIMIC database and a Chinese cohort.Frontiers in endocrinology · 2026Article
- Inflammation and nutrition-derived indicators for predicting amputation risk in patients with type 2 diabetic foot ulcers.Frontiers in nutrition · 2026Article
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- Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.Annals of medicine · 2025Article
- Integrating image processing with deep convolutional neural networks for gene selection and cancer classification using microarray data.Scientific reports · 2025Article
- Computational tracking of cell origins using CellSexID from single-cell transcriptomes.Cell reports methods · 2025Article
- Machine learning-driven gene expression profiling for lung cancer stage determination.Cancer biomarkers : section A of Disease markers · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Identification of novel therapeutic targets in hepatitis-B virus-associated membranous nephropathy using scRNA-seq and machine learning.Scientific reports · 2025Article
- An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.Frontiers in endocrinology · 2025Article
- Social anxiety prediction model for nursing students based on machine learning: a cross-sectional survey.Frontiers in psychiatry · 2025Article
- A comprehensive learning based swarm optimization approach for feature selection in gene expression data.Heliyon · 2024Article
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4 authors.
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Abstract
Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.
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