ArticleBMC bioinformatics2012
Gene network modular-based classification of microarray samples.
Article in BMC bioinformatics, 2012. 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.
- Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.BioMed research international · 2016Article
- A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.BMC bioinformatics · 2015Article
- Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways.Cancer informatics · 2014Review
- Systems genetics in "-omics" era: current and future development.Theory in biosciences = Theorie in den Biowissenschaften · 2013Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundMolecular predictor is a new tool for disease diagnosis, which uses gene expression to classify diagnostic category of a patient. The statistical challenge for constructing such a predictor is that there are thousands of genes to predict for the disease categories, but only a small number of samples are available.
resultsWe proposed a gene network modular-based linear discriminant analysis approach by integrating 'essential' correlation structure among genes into the predictor in order that the modules or cluster structures of genes, which are related to the diagnostic classes we look for, can have potential biological interpretation. We evaluated performance of the new method with other established classification methods using three real data sets.
conclusionsOur results show that the new approach has the advantage of computational simplicity and efficiency with relatively lower classification error rates than the compared methods in many cases. The modular-based linear discriminant analysis approach induced in the study has the potential to increase the power of discriminant analysis for which sample sizes are small and there are large number of genes in the microarray studies.
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