ArticleBMC bioinformatics2014
Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.Frontiers in digital health · 2023Article
- Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration.International journal of molecular sciences · 2022Article
- A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data.BMC bioinformatics · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer.Scientific reports · 2022Article
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Particle swarm optimization artificial intelligence technique for gene signature discovery in transcriptomic cohorts.Computational and structural biotechnology journal · 2022Article
- Determination of biomarkers from microarray data using graph neural network and spectral clustering.Scientific reports · 2021Article
- ILRC: a hybrid biomarker discovery algorithm based on improved L1 regularization and clustering in microarray data.BMC bioinformatics · 2021Article
- A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data.BMC bioinformatics · 2021Article
- Categorizing SHR and WKY rats by chi2 algorithm and decision tree.Scientific reports · 2021Article
- Gene Expression Profiling of Early Acute Febrile Stage of Dengue Infection and Its Comparative Analysis WithFrontiers in cellular and infection microbiology · 2021Article
- In-Silico Study of Immune System Associated Genes in Case of Type-2 Diabetes With Insulin Action and Resistance, and/or Obesity.Frontiers in endocrinology · 2021Article
- Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions.Frontiers in genetics · 2020Review
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk.Molecular oncology · 2019Article
- A novel gene selection algorithm for cancer classification using microarray datasets.BMC medical genomics · 2019Article
- Gene selection for microarray data classification via subspace learning and manifold regularization.Medical & biological engineering & computing · 2018Article
- Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction.BioData mining · 2018Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.
resultsTo achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.
conclusionBased on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.
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