ArticleCancer informatics2009
Microarray-based cancer prediction using soft computing approach.
Article in Cancer informatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Proteomic remodeling during tumor cell-induced platelet aggregation unveils metastatic drivers in colorectal cancer.Cancer cell international · 2026Article
- Article
- A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A framework model using multifilter feature selection to enhance colon cancer classification.PloS one · 2021Article
- Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile.Saudi journal of biological sciences · 2018Article
- DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles.Saudi journal of biological sciences · 2018Article
- mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.BioMed research international · 2015Article
- Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach.Current bioinformatics · 2014Article
- Rough set soft computing cancer classification and network: one stone, two birds.Cancer informatics · 2010Article
- Inference of cancer-specific gene regulatory networks using soft computing rules.Gene regulation and systems biology · 2010Article
- A robust gene selection method for microarray-based cancer classification.Cancer informatics · 2010Article
- Accurate molecular classification of cancer using simple rules.BMC medical genomics · 2009Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.
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Registered trials
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