Abstract
BackgroundNow multiple types of data are available for identifying disease genes. Those data include gene-disease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc.. It is believed that integrating different kinds of biological data is an effective method to identify disease genes.ResultsIn this paper, we propose a multiple data integration method based on the theory of Markov random field (MRF) and the method of Bayesian analysis for identifying human disease genes. The proposed method is not only flexible in easily incorporating different kinds of data, but also reliable in predicting candidate disease genes.ConclusionsNumerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. Predictions are evaluated by the leave-one-out method. The proposed method achieves an AUC score of 0.743 when integrating all those biological data in our experiments.
Highlights
Multiple types of data are available for identifying disease genes
The principle is largely supported by many biological data sources, such as protein-protein interactions (PPIs) [7,8,9,10,11], pathways [12,13,14,15], gene expression profiles [16,17,18], etc
A Markov random field (MRF) model is introduced to solve this problem by integrating multiple kinds of biological data, including known gene-disease associations, protein complexes, PPIs, pathways and gene expression profiles
Summary
Multiple types of data are available for identifying disease genes. Those data include genedisease associations, disease phenotype similarities, protein-protein interactions, pathways, gene expression profiles, etc. Many human genetic diseases or disorders are resulted from mutations of multiple genes [1]. The principle is largely supported by many biological data sources, such as protein-protein interactions (PPIs) [7,8,9,10,11], pathways [12,13,14,15], gene expression profiles [16,17,18], etc. Wu et al [5] develop a tool called CIPHER to predict disease genes based on a global
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