PuSH - Publikationsserver des Helmholtz Zentrums München

Chlis, N.-K. ; Bei, E.S.* ; Zervakis, M.*

Introducing a stable bootstrap validation framework for reliable genomic signature extraction.

IEEE/ACM Trans. Comput. Biol. Bioinform. 15, 181-190 (2016)
Verlagsversion DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Bioinformatics ; Classification ; Dna Microarrays ; Feature Selection ; Machine Learning ; Relevance Vector Machine (rvm) ; Support Vector Machine (svm); Breast-cancer; Bipolar Disorder; Gene-expression; Set; Metabolism; Brain; Pathophysiology; Classification; Abnormalities; Association
ISSN (print) / ISBN 1545-5963
e-ISSN 1557-9964
Quellenangaben Band: 15, Heft: 1, Seiten: 181-190 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort Los Alamitos