BACKGROUND: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. RESULTS: Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. CONCLUSIONS: The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.
BACKGROUND: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. RESULTS: Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. CONCLUSIONS: The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.
Authors: Christine Desmedt; Fanny Piette; Sherene Loi; Yixin Wang; Françoise Lallemand; Benjamin Haibe-Kains; Giuseppe Viale; Mauro Delorenzi; Yi Zhang; Mahasti Saghatchian d'Assignies; Jonas Bergh; Rosette Lidereau; Paul Ellis; Adrian L Harris; Jan G M Klijn; John A Foekens; Fatima Cardoso; Martine J Piccart; Marc Buyse; Christos Sotiriou Journal: Clin Cancer Res Date: 2007-06-01 Impact factor: 12.531
Authors: Brenda J Boersma; Mark Reimers; Ming Yi; Joseph A Ludwig; Brian T Luke; Robert M Stephens; Harry G Yfantis; Dong H Lee; John N Weinstein; Stefan Ambs Journal: Int J Cancer Date: 2008-03-15 Impact factor: 7.396
Authors: Tanya Barrett; Dennis B Troup; Stephen E Wilhite; Pierre Ledoux; Dmitry Rudnev; Carlos Evangelista; Irene F Kim; Alexandra Soboleva; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Rolf N Muertter; Ron Edgar Journal: Nucleic Acids Res Date: 2008-10-21 Impact factor: 16.971
Authors: Yudi Pawitan; Judith Bjöhle; Lukas Amler; Anna-Lena Borg; Suzanne Egyhazi; Per Hall; Xia Han; Lars Holmberg; Fei Huang; Sigrid Klaar; Edison T Liu; Lance Miller; Hans Nordgren; Alexander Ploner; Kerstin Sandelin; Peter M Shaw; Johanna Smeds; Lambert Skoog; Sara Wedrén; Jonas Bergh Journal: Breast Cancer Res Date: 2005-10-03 Impact factor: 6.466
Authors: H Bonsang-Kitzis; B Sadacca; A S Hamy-Petit; M Moarii; A Pinheiro; C Laurent; F Reyal Journal: Oncoimmunology Date: 2015-06-24 Impact factor: 8.110