Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets
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{"title"=>"Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets", "type"=>"journal", "authors"=>[{"first_name"=>"Silpa", "last_name"=>"Suthram", "scopus_author_id"=>"9248985000"}, {"first_name"=>"Joel T.", "last_name"=>"Dudley", "scopus_author_id"=>"18633685600"}, {"first_name"=>"Annie P.", "last_name"=>"Chiang", "scopus_author_id"=>"35335878000"}, {"first_name"=>"Rong", "last_name"=>"Chen", "scopus_author_id"=>"55141261100"}, {"first_name"=>"Trevor J.", "last_name"=>"Hastie", "scopus_author_id"=>"7004744483"}, {"first_name"=>"Atul J.", "last_name"=>"Butte", "scopus_author_id"=>"7003333396"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"358387539", "isbn"=>"1553-7358 (Electronic)\\n1553-734X (Linking)", "issn"=>"1553734X", "doi"=>"10.1371/journal.pcbi.1000662", "scopus"=>"2-s2.0-77649222972", "pmid"=>"20140234", "sgr"=>"77649222972"}, "id"=>"d94ab593-0602-3710-8c7a-9e0909b74ebc", "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.", "link"=>"http://www.mendeley.com/research/networkbased-elucidation-human-disease-similarities-reveals-common-functional-modules-enriched-pluri", "reader_count"=>295, "reader_count_by_academic_status"=>{"Unspecified"=>9, "Professor > Associate Professor"=>29, "Librarian"=>2, "Researcher"=>85, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>90, "Student > Postgraduate"=>10, "Other"=>20, "Student > Master"=>21, "Student > Bachelor"=>6, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>9, "Professor > Associate Professor"=>29, "Librarian"=>2, "Researcher"=>85, "Student > Doctoral Student"=>11, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/864336"], "description"=>"<p>This table shows only 3 of the 19 disease correlations significantly sharing at least one disease gene. The complete list is in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662#pcbi.1000662.s006\" target=\"_blank\">Table S3</a>. The “correlation” column indicates the calculated MRS-based correlation between the respective diseases. The number of genes whose variants are associated with any given disease was obtained from the Disease Gene List. We calculated the hypergeometric p-value by estimating the probability of seeing the observed overlap or more by chance accounting for the total number of disease genes present in our dataset (N = 2,104).</p>", "links"=>[], "tags"=>["computational biology/systems biology", "pathology/molecular pathology", "pharmacology/drug development"], "article_id"=>534779, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.t002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genetic_similarity_between_significant_disease_correlations_/534779", "title"=>"Genetic similarity between significant disease correlations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-02-05 01:19:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/864138"], "description"=>"<p>(A) Two representative samples of functional modules. (i) The synaptic vesicle module is one of the most down-regulated modules among set of brain disorders: Alzheimer's disease, Bipolar disorder, Schizophrenia and Glioblastoma. (ii) The DNA repair module is one of the most up-regulated modules among the lung cancers and Uterine leioyomyoma. The colors of the nodes represent their average gene expression in their corresponding diseases. The genes marked with a red star next to them are genes with known variants associated with disease (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662#s3\" target=\"_blank\">methods</a>). (B) A representative sample of common disease “signature” modules. The genes colored in orange correspond to known drug targets. The functions for the modules were obtained by the functional enrichment tool of the DAVID database <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662#pcbi.1000662-Huangda1\" target=\"_blank\">[45]</a>.</p>", "links"=>[], "tags"=>["computational biology/systems biology", "pathology/molecular pathology", "pharmacology/drug development"], "article_id"=>534586, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Underlying_functional_modules_/534586", "title"=>"Underlying functional modules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-02-05 01:16:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/863873"], "description"=>"<p>(A) Normalization of the gene expression matrices through a Z-score transformation. In the gene expression matrix for a given disease k, g<sub>ij</sub> represents the expression value of gene i in sample j, g<sub>j</sub> corresponds to the whole set of gene expression values for a given sample j (j<sup>th</sup> column) and z<sub>ij</sub> corresponds to the z-score transformed gene expression value of gene i sample j. (B) Response score of a gene in a given disease. The response score of gene i in a disease k is the t-test statistic between the disease and control sample values for that gene. This score is represented as S<sub>ik</sub>. (C) Module response score calculation. The Module Response Score (MRS) of a given module i in a given disease k (M<sub>ik</sub>) is average of the response scores of its component genes. Detailed description of this process is provided in the <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662#s3\" target=\"_blank\">Methods</a> section.</p>", "links"=>[], "tags"=>["module", "scores"], "article_id"=>534315, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.g001", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_process_to_generate_the_module_response_scores_for_each_disease_/534315", "title"=>"Overview of the process to generate the module response scores for each disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-02-05 01:11:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/428971", "https://ndownloader.figshare.com/files/429017", "https://ndownloader.figshare.com/files/429074", "https://ndownloader.figshare.com/files/429111", "https://ndownloader.figshare.com/files/429126", "https://ndownloader.figshare.com/files/429138", "https://ndownloader.figshare.com/files/429156"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["network-based", "elucidation", "similarities", "reveals", "modules", "enriched", "pluripotent", "targets"], "article_id"=>144700, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000662.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000662.s007"], "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Network_Based_Elucidation_of_Human_Disease_Similarities_Reveals_Common_Functional_Modules_Enriched_for_Pluripotent_Drug_Targets/144700", "title"=>"Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-02-05 01:18:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/864292"], "description"=>"<p>This table shows only 3 of the 14 significant disease correlations that significantly shared at least one drug. The complete list is in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000662#pcbi.1000662.s007\" target=\"_blank\">Table S4</a>. The “correlation” column indicates the calculated correlation between the respective diseases. We calculated the hypergeometric p-value by estimating probability of seeing the observed overlap or more by chance accounting for the total number of drugs in our knowledge base (N = 3,536).</p>", "links"=>[], "tags"=>["drugs"], "article_id"=>534744, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.t003", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shared_drugs_among_significant_disease_correlations_/534744", "title"=>"Shared drugs among significant disease correlations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-02-05 01:19:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/864005"], "description"=>"<p>(A) Hierarchical clustering of the disease correlations. The distance between two diseases was defined to be (1-correlation coefficient) of the two diseases. The tree was constructed using the average method of hierarchical clustering. The red line corresponds to a <i>p</i>-value of 0.01 and FDR of 10.37% and, disease correlations below this line are considered significant. The different colors represent the various categories of significant disease correlations. (B) The network of all the 138 significant disease correlations. The colors correspond to significant disease correlation categories in (A). The nodes colored in grey are not marked in (A).</p>", "links"=>[], "tags"=>["disease-disease"], "article_id"=>534454, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_disease_disease_similarities_/534454", "title"=>"Significant disease-disease similarities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-02-05 01:14:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/864252"], "description"=>"<p>We performed a one-sided Fisher's Exact Test on this table giving a <i>p</i>-value of 0.033.</p>", "links"=>[], "tags"=>["correlations"], "article_id"=>534707, "categories"=>["Pharmacology", "Medicine"], "users"=>["Silpa Suthram", "Joel T. Dudley", "Annie P. Chiang", "Rong Chen", "Trevor J. Hastie", "Atul J. Butte"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000662.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contingency_table_to_evaluate_the_hypothesis_that_significant_disease_correlations_also_significantly_shared_disease_genes_/534707", "title"=>"Contingency table to evaluate the hypothesis that significant disease correlations also significantly shared disease genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-02-05 01:18:27"}

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