Identifying pair-wise gene functional similarity by multiplex gene expression maps and supervised learning

Authors:
Li An;Haibin Ling;Zoran Obradovic;Desmond J. Smith;Vasileios Megalooikonomou
Affiliations:
Temple University, Philadelphia, PA;Temple University, Philadelphia, PA;Temple University, Philadelphia, PA;David Geffen School of Medicine, UCLA, CHS, Los Angeles, CA;Temple University, Philadelphia, PA
Venue:
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2011

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Abstract

Research has been done to explore the relationships between the Gene Ontology-based similarity and gene expression profiles in the mammalian brain. However, little attention has been paid to the location information of a gene's expressions. Gene expression maps, which contain spatial information regarding the expression of genes in mice's brain, are obtained by combining voxelation and microarrays. Based on the hypothesis that genes with similar gene expression maps may have similar gene functions, we propose an approach to identify pair-wise gene functional similarities by gene expression maps. By considering pairs of genes from an original dataset as samples whose features are extracted from expression maps and labels are the functional similarities of pairs of genes, we explore the relationship between similarities of gene maps and gene functions. We restrict the dataset to genes that are associated with previously detected functional expression profiles to strengthen the relationship. We use AdaBoost, coupled with our proposed weak classifier, to analyze the dataset and predict the functional similarities. The experimental results show that with the increasing similarities of gene expression maps, the functional similarities are increased too. The boosting analysis can predict the functional similarities between genes to a certain degree. The weights of the features in the model indicate which features are significant for this prediction. These findings can potentially assist the biologists by providing helpful clues in predicting gene functions.