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This article is part of the supplement: ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011

Open Access Proceedings

Learning pair-wise gene functional similarity by multiplex gene expression maps

Li An12*, Haibin Ling2, Zoran Obradovic2, Desmond J Smith3 and Vasileios Megalooikonomou12

Author affiliations

1 Data Engineering Laboratory, Department of Computer and Information Sciences, Temple University, PA, USA

2 Center for Data Analytics and Biomedical Informatics, Temple University, PA, USA

3 Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, CA, USA

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 3):S1  doi:10.1186/1471-2105-13-S3-S1

Published: 21 March 2012

Abstract

Background

The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute.

Results

Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction.

Conclusions

By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.