Integration of heterogeneous data sources for identification of disease genes using computational techniques

Genes related to causing some disease are called disease-causing genes or disease genes. In wet-lab experiments, disease genes are identified by mutation analysis, which is expensive and labor extensive. In this thesis, we propose novel computational techniques to predict disease genes. In the first part of this thesis, we proposed five novel topological features obtained from the Protein-Protein Interaction (PPI) network. We applied Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) networks to predict new cancer genes using these features. We found that SVM performed slightly better than MLP. We also found that the feature, named 2N-index, is the most discriminative feature between cancer genes and other genes. With the availability of various data sources related to genes and disease phenotype, accurate prediction of disease genes is possible by integrating the information available from multiple data sources. We propose several novel computational models to integrate multiple data sources for the identification of disease genes. These models are proposed to prioritize set of candidate disease genes, based on their functional similarity to known disease genes. The top ranked candidate gene is most likely to be the real disease gene. In the second part of this thesis, we focus on rank aggregation based integration viii (RABI) models. We integrated four genomic data sources, i.e., protein-protein interaction data, and three ontologies, Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). We first rank candidate genes based on each of the four data sources, using 1CSVM (one-class SVM) and RWR (Random Walk with Restart) algorithm. Then we use the NDOS (N-Dimensional Order Statistics) algorithm to combine the four rank lists into one list, which is used as the final prioritization of the set of candidate genes. Computational models were evaluated in terms of AUC value, using 36 diseases as the benchmark data. The proposed RWR+NDOS and 1CSVM models achieved AUC values of 85.1% and 84.4%, better than the Multiple Kernels Learning (MKL) method which gave an AUC of 79.3%. Next, we proposed another efficient rank aggregation algorithm named Discounted Rating System (DRS). The proposed RWR+DRS and 1CSVM+DRS models achieved AUC values of 87.7% and 85.4%, higher than the NDOS-based models. In the third part of this thesis, we focus on network based integration (NBI) models. We proposed an improved RWR algorithm named RWR on Multiple networks (RWRM), to integrate the four genomic data sources (PPI, BP, CC and MF). We obtained the AUC value of 89.4% on the same benchmark data as in the second part. In case of disease phenotype without any known disease genes, the candidate genes are prioritized based on their similarity to disease genes associated with similar phenotypes. We proposed a novel heterogeneous network model, named RWR for Heterogeneous network (RWRH), to integrate the PPI network and phenotype information obtained from OMIM. We have shown using the leave-one-out cross validation (LOO-CV) that, the RWRH algorithm correctly identified 814 of 1,428 phenotype-gene relationships (PGRs), the previous work identified ix only 709 of them [1]. Finally, we proposed a Complex Heterogeneous Network (CHN) model, in which four genomic data sources and the phenotype data were integrated. Both RWRM and RWRH algorithms are used in the CHN model to prioritize candidate genes. We used the latest OMIM data as benchmark data, containing 3,871 PGRs. We successfully identified 2,105 of them, whereas the DRS-based and NDOS-based integration methods, respectively, could identify 2,008 and 2,048 number of relationships. Thus, the proposed CHN model identified more PGRs than both DRS-based and NDOS-based integration methods, which showed better performance of the proposed CHN model.