research-article

Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction

Authors:

Nikolaos-Kosmas Chlis,

Ekaterini S. Bei,

Michalis ZervakisAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 15, Issue 1

Pages 181 - 190

https://doi.org/10.1109/TCBB.2016.2633267

Published: 01 January 2018 Publication History

Abstract

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 proposed method is also evaluated. The results of this study highlight the importance of stability issues in genomic signatures, beyond their prediction capabilities.

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Cited By

Sun JWang PYu HYang X(2023)A constraint score guided meta-heuristic searching to attribute reductionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22283244:3(4779-4800)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222832
Shen ZChen XGaribaldi J(2019)A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2019.8858890(1-6)Online publication date: 23-Jun-2019
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2019.8858890

Index Terms

Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 15, Issue 1

January 2018

352 pages

ISSN:1545-5963

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Copyright © 2018.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 January 2018

Published in TCBB Volume 15, Issue 1

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Cited By

Sun JWang PYu HYang X(2023)A constraint score guided meta-heuristic searching to attribute reductionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22283244:3(4779-4800)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222832
Shen ZChen XGaribaldi J(2019)A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2019.8858890(1-6)Online publication date: 23-Jun-2019
https://dl.acm.org/doi/10.1109/FUZZ-IEEE.2019.8858890

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