ArticleBMC bioinformatics2019
An efficient gene selection method for microarray data based on LASSO and BPSO.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases.Journal of clinical and translational pathology · 2026Article
- Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization.PeerJ. Computer science · 2025Article
- Genome-wide DNA methylation analysis of hippocampal tissue in a murine model of attention deficit-hyperactivity disorder.PloS one · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis.Botanical studies · 2024Article
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- A data science approach to optimize ADHD assessment with the BRIEF-2 questionnaire.Translational neuroscience · 2024Article
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- A novel approach for the analysis of single-cell RNA sequencing identifies TMEM14B as a novel poor prognostic marker in hepatocellular carcinoma.Scientific reports · 2023Article
- Predicting gene knockout effects from expression data.BMC medical genomics · 2023Article
- Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare.Journal of translational medicine · 2023Article
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- Identification of key programmed cell death-related genes and immune infiltration in extracorporeal membrane oxygenation treatment for acute myocardial infarction based on bioinformatics analysis.Frontiers in cardiovascular medicine · 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
- Pancancer analysis of a potential gene mutation model in the prediction of immunotherapy outcomes.Frontiers in genetics · 2022Article
- A primer on machine learning techniques for genomic applications.Computational and structural biotechnology journal · 2021Review
- Development and Evaluation of a New Predictive Nomogram for Predicting Risk of Herpes Zoster Infection in a Chinese Population with Type 2 Diabetes Mellitus.Risk management and healthcare policy · 2021Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe main goal of successful gene selection for microarray data is to find compact and predictive gene subsets which could improve the accuracy. Though a large pool of available methods exists, selecting the optimal gene subset for accurate classification is still very challenging for the diagnosis and treatment of cancer.
resultsTo obtain the most predictive genes subsets without filtering out critical genes, a gene selection method based on least absolute shrinkage and selection operator (LASSO) and an improved binary particle swarm optimization (BPSO) is proposed in this paper. To avoid overfitting of LASSO, the initial gene pool is divided into clusters based on their structure. LASSO is then employed to select high predictive genes and further calculate the contribution value which indicates the genes' sensitivity to samples' classes. With the second-level gene pool established by double filter strategy, the BPSO encoding the contribution information obtained from LASSO is improved to perform gene selection. Moreover, from the perspective of the bit change probability, a new mapping function is defined to guide the updating of the particle to select the more predictive genes in the improved BPSO.
conclusionsWith the compact gene pool obtained by double filter strategies, the improved BPSO could select the optimal gene subsets with high probability. The experimental results on several public microarray data with extreme learning machine verify the effectiveness of the proposed method compared to the relevant methods.
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