title: Improving the accuracy of gene expression profile classification with Lorenz curves and Gini ratios.
abstract: Microarrays are a new technology with great potential to provide accurate medical diagnostics, help to find the right treatment for many diseases such as cancers, and provide a detailed genome-wide molecular portrait of cellular states. In this chapter, we show how Lorenz Curves and Gini Ratios can be modified to improve the accuracy of gene expression profile classification. Experimental results with different classification algorithms using additional techniques and strategies for improving the accuracy such as the principal component analysis, the correlation-based feature subset selection, and the consistency subset evaluation technique for the task of classifying lung adenocarcinomas from gene expression show that our method find more optimal genes than SAM.
authors: Department of Computer Science, Lamar University, Beaumont, TX 77710, USA. qntran@lamar.edu
authors: Quoc-Nam
authors: QN
authors: Tran
mesh_terms: D000230:Adenocarcinoma / Q000145:classification / Q000235:genetics; D000465:Algorithms; D015982:Bias; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000235:genetics; D025341:Principal Component Analysis