Improving biomarker list stability by integration of biological knowledge in the learning process

BMC Bioinformatics

Research

Andrea Bisognin¹  Barbara Di Camillo²  Tiziana Sanavia²  Giovanni Da San Martino³  Fabio Aiolli³ 

[1] Department of Biology, University of Padova, Via G. Colombo 3, 35121, Padova, Italy;Department of Information Engineering, University of Padova, via G. Gradenigo 6/B, 35131, Padova, Italy;Department of Pure and Applied Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy;

关键词: Gene Ontology;    Similarity Matrix;    Semantic Similarity;    Geodesic Distance;    Similarity Matrice;   

DOI  :  10.1186/1471-2105-13-S4-S22

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsBiological 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Sanavia et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.