ArticleComputational and mathematical methods in medicine2016
Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.
Article in Computational and mathematical methods in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization.Sensors (Basel, Switzerland) · 2022Article
- Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.Medical & biological engineering & computing · 2022Article
- Identifying common transcriptome signatures of cancer by interpreting deep learning models.Genome biology · 2022Article
- Particle swarm optimization artificial intelligence technique for gene signature discovery in transcriptomic cohorts.Computational and structural biotechnology journal · 2022Article
- A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.BMC medical informatics and decision making · 2021Article
- Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer.PeerJ. Computer science · 2021Article
- Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants.BMC bioinformatics · 2020Article
- RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA.Nature protocols · 2019Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson's Disease in LabVIEW Environment.Parkinson's disease · 2019Article
- Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis.BioMed research international · 2019Article
- Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.Computational and mathematical methods in medicine · 2018Article
- Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.Cancer cell · 2017Article
- DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2017Article
- Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features.Computational and mathematical methods in medicine · 2017Article
- A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.Computational intelligence and neuroscience · 2017Article
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5 authors.
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Abstract
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
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