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Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data

Paper ID Volume ID Publish Year Pages File Format Full-Text
15556 1427 2007 9 PDF Available
Title
Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data
Abstract

Class prediction based on DNA microarray data has been emerged as one of the most important application of bioinformatics for diagnostics/prognostics. Robust classifiers are needed that use most biologically relevant genes embedded in the data. A consensus approach that combines multiple classifiers has attributes that mitigate this difficulty compared to a single classifier. A new classification method named as consensus analysis of multiple classifiers using non-repetitive variables (CAMCUN) was proposed for the analysis of hyper-dimensional gene expression data. The CAMCUN method combined multiple classifiers, each of which was built from distinct, non-repeated genes that were selected for effectiveness in class differentiation. Thus, the CAMCUN utilized most biologically relevant genes in the final classifier. The CAMCUN algorithm was demonstrated to give consistently more accurate predictions for two well-known datasets for prostate cancer and leukemia. Importantly, the CAMCUN algorithm employed an integrated 10-fold cross-validation and randomization test to assess the degree of confidence of the predictions for unknown samples.

Keywords
Consensus approach; Microarray gene expression; Diagnostics; Prognostics
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Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 31, Issue 1, February 2007, Pages 48–56
Authors
, , , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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