Browsing by Author "Xie, MaoQiang"

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    Inferring Disease and Gene Set Associations with Rank Coherence in Networks
    (2011-01-18) Hwang, TaeHyun; Zhang, Wei; Xie, MaoQiang; Kuang, Rui
    A computational challenge to validate the candidate disease genes identi?ed in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An e?cient algorithm coupling ridge regression with label propagation, and two variants are introduced to ?nd the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered disease-gene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of GWAS, DNA copy number variation analysis, and gene expression pro?ling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. The rcNet algorithms are available as a webtool for disease and gene set association analysis at http://compbio.cs.umn.edu/dgsa_rcNet.
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    Reconstructing Disease Phenome-genome Association by Bi-Random Walk
    (2013-08-16) Xie, MaoQiang; Hwang, TaeHyun; Kuang, Rui
    Promising results were recently reported in utilizing network information in phenotype-similarity network and gene-interaction network with graph-based learning to derive new disease phenotype-gene associations. However, a more fundamental understanding of how the network information is relevant to disease phenotype-gene associations is lacking. In this paper, we analyze the circular bigraphs (CBGs) in OMIM phenotype-gene association networks, and introduce a bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks for unveiling the associations between the complete collection of disease phenotypes (phenome) and genes. BiRW performs separate random walk simultaneously on gene interaction network and phenotype similarity network to explore gene paths and phenotype paths in CBGs of different sizes. In the analysis of OMIM associations, we discovered that 81% of the associations are covered by CBG patterns of path-length up to 3 with variability by 21 disease classes, and there is a clear correlation between the CBG coverage and the predictability of the phenotype-gene associations. Some prominent examples are cancers, nutritional diseases, dermatological diseases, bone diseases, cardiovascular diseases and respiratory diseases. Experiments on recovering known associations in cross-validation and predicting new associations in a test set validated that BiRW effectively improved prediction performance over existing methods by ranking more known associations in the top 100 out of more than 12,000 candidate genes. The investigation of the global disease phenome-genome association map also revealed interesting new predictions and phenotype-gene modules by disease classes. Availability: http://compbio.cs.umn.edu/BiRW