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Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction by Nikolaos-Kosmas Chlis; Ekaterini S. Bei; Michalis Zervakis is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data. That instability hinders the validity of research findings and raises skepticism about the reliability of such methods. In this study, a complete framework for the extraction of stable and reliable lists of candidate genes is presented. The proposed methodology enforces stability of results at the validation step and as a result, it is independent of the feature selection and classification methods used. Furthermore, two different statistical tests are performed in order to assess the statistical significance of the observed results. Moreover, the consistency of the signatures extracted by independent executions of the propose

Who reads Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction?

Author

Nikolaos-Kosmas Chlis; Ekaterini S. Bei; Michalis Zervakis

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2016

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)