%0 Journal Article %1 Suthram2010NetworkBased %A Suthram, Silpa %A Dudley, Joel T. %A Chiang, Annie P. %A Chen, Rong %A Hastie, Trevor J. %A Butte, Atul J. %D 2010 %I Public Library of Science %J PLoS Comput Biol %K disease drug-targets networks %N 2 %P e1000662+ %R 10.1371/journal.pcbi.1000662 %T Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets %U http://dx.doi.org/10.1371/journal.pcbi.1000662 %V 6 %X Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state ” signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities. Many human diseases are related to each other through shared causes or even shared pathology. Knowledge of these relationships has long been exploited to treat similar diseases with the same therapies. However, most of the traditional approaches to discover these relationships have depended on subjective measures, such as similarity in symptoms, or incomplete knowledge, such as genes with mutations. Here we present the first approach integrating high-throughput datasets such as mRNA expression and large-scale protein-protein interaction networks to discover human disease relationships in a systematic and quantitative way. We discover 138 significant pathological similarities between 54 human diseases ranging from lung cancer, schizophrenia, and malaria. We also discovered a set of common pathways and processes within the cell that are dysregulated in at least half of the diseases. We infer that these processes correspond to a common response of the human system to a disease state. Interestingly, we find that many of the proteins in these pathways are already known to be targets of existing drugs. In fact, the drugs corresponding to these proteins are known to treat significantly more diseases than expected by chance highlighting the importance of these common molecular pathological pathways as prime therapeutic opportunities.

@article{Suthram2010NetworkBased, abstract = {Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related {mRNA} expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state ” signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities. Many human diseases are related to each other through shared causes or even shared pathology. Knowledge of these relationships has long been exploited to treat similar diseases with the same therapies. However, most of the traditional approaches to discover these relationships have depended on subjective measures, such as similarity in symptoms, or incomplete knowledge, such as genes with mutations. Here we present the first approach integrating high-throughput datasets such as {mRNA} expression and large-scale protein-protein interaction networks to discover human disease relationships in a systematic and quantitative way. We discover 138 significant pathological similarities between 54 human diseases ranging from lung cancer, schizophrenia, and malaria. We also discovered a set of common pathways and processes within the cell that are dysregulated in at least half of the diseases. We infer that these processes correspond to a common response of the human system to a disease state. Interestingly, we find that many of the proteins in these pathways are already known to be targets of existing drugs. In fact, the drugs corresponding to these proteins are known to treat significantly more diseases than expected by chance highlighting the importance of these common molecular pathological pathways as prime therapeutic opportunities.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Suthram, Silpa and Dudley, Joel T. and Chiang, Annie P. and Chen, Rong and Hastie, Trevor J. and Butte, Atul J.}, biburl = {https://www.bibsonomy.org/bibtex/260171373a39b4a8ae8704f0879ec32f3/karthikraman}, citeulike-article-id = {6629398}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000662}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/20140234}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=20140234}, day = 5, doi = {10.1371/journal.pcbi.1000662}, interhash = {12a25a855dff421132163acbd30230a6}, intrahash = {60171373a39b4a8ae8704f0879ec32f3}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {disease drug-targets networks}, month = feb, number = 2, pages = {e1000662+}, pmid = {20140234}, posted-at = {2010-02-05 09:03:36}, priority = {5}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {{Network-Based} Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000662}, volume = 6, year = 2010 }