%0 Journal Article %1 Fang2013Diseasedrugphenotype %A Fang, Hai %A Gough, Julian %D 2013 %I The Royal Society of Chemistry %J Mol. BioSyst. %K disease-networks drug-targets networks %N 7 %P 1686--1696 %R 10.1039/c3mb25495j %T A disease-drug-phenotype matrix inferred by walking on a functional domain network %U http://dx.doi.org/10.1039/c3mb25495j %V 9 %X Protein domains are classified as units of structure, evolution and function, and thus form the molecular backbone of biosphere. Although functional networks at the protein level have been reported to be of value in predicting diseases (phenotypes or drugs), they have not previously been applied at the sub-protein resolution (protein domain in this case). We herein introduce a domain network with a functional perspective. This network has nodes consisting of protein domains (at the superfamily/evolutionary level), with edges weighted by the semantic similarity according to domain-centric Gene Ontology (dcGO) annotations, which henceforth we call "dcGOnet". By globally exploring this network via a random walk, we demonstrate its predictive value on disease, drug, or phenotype-related ontologies. On cross-validation recovering ontology labels for domains, we achieve an overall area under the ROC curve of 89.0\% for drugs, 87.3\% for diseases, 87.6\% for human phenotypes and 88.2\% for mouse phenotypes. We show that the performance using global information from this network is significantly better than using local information, and also illustrate that the better performance is not sensitive to network size, or the choice of algorithm parameters, and is universal to different ontologies. Based on the dcGOnet and its global properties, we further develop an approach to build a disease-drug-phenotype matrix. The predicted interconnections are statistically supported using a novel randomization procedure, and are also empirically supported by inspection for biological relevance. Most of the high-ranking predictions recover connections that are well known, but others uncover connections that have only suggestive or obscure support in the literature; we show that these are missed by simpler methods, in particular for drug-disease connections. The value of this work is threefold: we describe a general methodology and make the software available, we provide the functional domain network itself, and the ranked drug-disease-phenotype matrix provides rich targets for investigation. All three can be found at http://supfam.org/SUPERFAMILY/dcGO/dcGOnet.html.

@article{Fang2013Diseasedrugphenotype, abstract = {Protein domains are classified as units of structure, evolution and function, and thus form the molecular backbone of biosphere. Although functional networks at the protein level have been reported to be of value in predicting diseases (phenotypes or drugs), they have not previously been applied at the sub-protein resolution (protein domain in this case). We herein introduce a domain network with a functional perspective. This network has nodes consisting of protein domains (at the superfamily/evolutionary level), with edges weighted by the semantic similarity according to domain-centric Gene Ontology ({dcGO}) annotations, which henceforth we call "{dcGOnet}". By globally exploring this network via a random walk, we demonstrate its predictive value on disease, drug, or phenotype-related ontologies. On cross-validation recovering ontology labels for domains, we achieve an overall area under the {ROC} curve of 89.0\% for drugs, 87.3\% for diseases, 87.6\% for human phenotypes and 88.2\% for mouse phenotypes. We show that the performance using global information from this network is significantly better than using local information, and also illustrate that the better performance is not sensitive to network size, or the choice of algorithm parameters, and is universal to different ontologies. Based on the {dcGOnet} and its global properties, we further develop an approach to build a disease-drug-phenotype matrix. The predicted interconnections are statistically supported using a novel randomization procedure, and are also empirically supported by inspection for biological relevance. Most of the high-ranking predictions recover connections that are well known, but others uncover connections that have only suggestive or obscure support in the literature; we show that these are missed by simpler methods, in particular for drug-disease connections. The value of this work is threefold: we describe a general methodology and make the software available, we provide the functional domain network itself, and the ranked drug-disease-phenotype matrix provides rich targets for investigation. All three can be found at {http://supfam.org/SUPERFAMILY}/{dcGO}/{dcGOnet}.html.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Fang, Hai and Gough, Julian}, biburl = {https://www.bibsonomy.org/bibtex/2d0d9d2afd31ce38fac9b3afbf3fd066a/karthikraman}, citeulike-article-id = {12211226}, citeulike-linkout-0 = {http://dx.doi.org/10.1039/c3mb25495j}, citeulike-linkout-1 = {http://www.rsc.org/Publishing/Journals/article.asp?doi=C3MB25495J}, doi = {10.1039/c3mb25495j}, interhash = {0c2f152b20e679d5998e47506ec0d40a}, intrahash = {d0d9d2afd31ce38fac9b3afbf3fd066a}, journal = {Mol. BioSyst.}, keywords = {disease-networks drug-targets networks}, number = 7, pages = {1686--1696}, posted-at = {2013-03-26 05:50:46}, priority = {2}, publisher = {The Royal Society of Chemistry}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {A disease-drug-phenotype matrix inferred by walking on a functional domain network}, url = {http://dx.doi.org/10.1039/c3mb25495j}, volume = 9, year = 2013 }