ArticleIET systems biology2016
Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification.
Article in IET systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection.iScience · 2023Article
- Mutational Slime Mould Algorithm for Gene Selection.Biomedicines · 2022Article
- Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification.Journal of medical systems · 2019Article
- Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy.IET systems biology · 2018Article
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2 authors.
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Abstract
A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective.
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