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Volumn 6, Issue , 2017, Pages

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOINFORMATICS; COMPUTER MODEL; CONTROLLED STUDY; DOWN REGULATION; DRUG EFFICACY; DRUG REPOSITIONING; EPILEPSY; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENE MAPPING; GENE ONTOLOGY; GENE OVEREXPRESSION; GENE SILENCING; MACHINE LEARNING; REAL TIME POLYMERASE CHAIN REACTION; TOBACCO DEPENDENCE; TRANSCRIPTION REGULATION; UPREGULATION; BIOLOGICAL MODEL; BIOLOGY; DRUG DEVELOPMENT; HUMAN; PROCEDURES; SYSTEMS BIOLOGY;

EID: 85032925956     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.26726     Document Type: Article
Times cited : (386)

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    • Himmelstein D, Greene C, Baranzini S. 2015b. Renaming “Heterogeneous Networks” to a More Concise and Catchy Term. ThinkLab. https://doi.org/10.15363/thinklab.d104 [Accessed September 11, 2017].
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    • Positive correlations between knockdown and overexpression profiles from LINCS L1000
    • Accessed September 11, 2017
    • Himmelstein D, Greene C, Jensen LJ. 2016o. Positive correlations between knockdown and overexpression profiles from LINCS L1000. ThinkLab. https://doi.org/10.15363/thinklab.d171 [Accessed September 11, 2017].
    • (2016) Thinklab
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  • 69
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    • Compiling Gene Ontology annotations into an easy-to-use format
    • Accessed September 11, 2017
    • Himmelstein D, Greene C, Malladi V, Bastian F. 2015g. Compiling Gene Ontology annotations into an easy-to-use format. ThinkLab. https://doi.org/10.15363/thinklab.d39 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 70
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    • Using Entrez Gene as our gene vocabulary
    • Accessed September 11, 2017
    • Himmelstein D, Greene C, Pico A. 2015h. Using Entrez Gene as our gene vocabulary. ThinkLab. https://doi.org/10.15363/thinklab.d34 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 71
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    • Dhimmel/Stargeo V1.0: Differentially Expressed Genes For 48 Diseases From Stargeo
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  • 72
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    • Creating a catalog of protein interactions
    • Accessed September 11, 2017
    • Himmelstein D, Hadley D, Strokach A. 2015z. Creating a catalog of protein interactions. ThinkLab. https://doi. org/10.15363/thinklab.d85 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 73
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    • Predictions of whether a compound treats a disease
    • Accessed September 11, 2017
    • Himmelstein D, Hessler C, Khankhanian P. 2016a. Predictions of whether a compound treats a disease. ThinkLab. https://doi.org/10.15363/thinklab.d203 [Accessed September 11, 2017].
    • (2016) Thinklab
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  • 74
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    • Data nomenclature: Naming and abbreviating our network types
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    • Himmelstein D, Jensen LJ, Khankhanian P. 2016c. Data nomenclature: naming and abbreviating our network types. ThinkLab. https://doi.org/10.15363/thinklab.d162 [Accessed September 11, 2017].
    • (2016) Thinklab
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  • 75
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    • Integrating resources with disparate licensing into an open network
    • Accessed September 11, 2017
    • Himmelstein D, Jensen LJ, Smith M, Fortney K, Chung C. 2015i. Integrating resources with disparate licensing into an open network. ThinkLab. https://doi.org/10.15363/thinklab.d107 [Accessed September 11, 2017].
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  • 76
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    • Gene-Tissue Relationships From The Tissues Database
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    • (2015) Zenodo
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  • 77
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    • The TISSUES resource for the tissue-specificity of genes
    • Accessed September 11, 2017
    • Himmelstein D, Jensen LJ. 2015h. The TISSUES resource for the tissue-specificity of genes. ThinkLab. https://doi. org/10.15363/thinklab.d91 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 78
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    • Processing the DISEASES resource for disease-gene relationships
    • Accessed September 11, 2017
    • Himmelstein D, Jensen LJ. 2015l. Processing the DISEASES resource for disease-gene relationships. ThinkLab. https://doi.org/10.15363/thinklab.d106 [Accessed September 11, 2017].
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  • 79
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    • One network to rule them all
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    • (2015) Thinklab
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  • 81
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    • Expert curation of our indication catalog for disease-modifying treatments
    • Accessed September 11, 2017
    • Himmelstein D, Khankhanian P, Hessler C. 2015j. Expert curation of our indication catalog for disease-modifying treatments. ThinkLab. https://doi.org/10.15363/thinklab.d95 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 83
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    • Transforming DWPCs for hetnet edge prediction
    • Accessed September 11, 2017
    • Himmelstein D, Khankhanian P, Lizee A. 2016s. Transforming DWPCs for hetnet edge prediction. ThinkLab. https://doi.org/10.15363/thinklab.d193 [Accessed September 11, 2017].
    • (2016) Thinklab
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  • 84
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    • Visualizing the top epilepsy predictions in Cytoscape
    • Accessed September 11, 2017
    • Himmelstein D, Khankhanian P, Pico A, Jensen LJ, Morris S. 2017a. Visualizing the top epilepsy predictions in Cytoscape. ThinkLab. https://doi.org/10.15363/thinklab.d230 [Accessed September 11, 2017].
    • (2017) Thinklab
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  • 85
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    • Processing LabeledIn to extract indications
    • Accessed September 11, 2017
    • Himmelstein D, Khare R. 2015s. Processing LabeledIn to extract indications. ThinkLab. https://doi.org/10.15363/thinklab.d46 [Accessed September 11, 2017].
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  • 86
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    • Unifying disease vocabularies
    • Accessed September 11, 2017
    • Himmelstein D, Li TS. 2015d. Unifying disease vocabularies. ThinkLab. https://doi.org/10.15363/thinklab.d44 [Accessed September 11, 2017].
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  • 90
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    • Computing standardized logistic regression coefficients
    • Accessed September 11, 2017
    • Himmelstein D, Lizee A. 2016a. Computing standardized logistic regression coefficients. ThinkLab. https://doi. org/10.15363/thinklab.d205 [Accessed September 11, 2017].
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  • 91
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    • Estimating the complexity of hetnet traversal
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    • Himmelstein D, Lizee A. 2016t. Estimating the complexity of hetnet traversal. ThinkLab. https://doi.org/10.15363/thinklab.d187 [Accessed September 11, 2017].
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  • 92
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    • Measuring user contribution and content creation
    • Accessed September 11, 2017
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  • 93
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    • Mining knowledge from MEDLINE articles and their indexed MeSH terms
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    • Himmelstein D, Pankov A. 2015a. Mining knowledge from MEDLINE articles and their indexed MeSH terms. ThinkLab. https://doi.org/10.15363/thinklab.d67 [Accessed September 11, 2017].
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  • 94
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    • Selecting informative ERC (Evolutionary rate covariation) values between genes
    • Accessed September 11, 2017
    • Himmelstein D, Partha R. 2015r. Selecting informative ERC (evolutionary rate covariation) values between genes. ThinkLab. https://doi.org/10.15363/thinklab.d57 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 95
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    • Protein (Target, carrier, transporter, and enzyme) interactions in DrugBank
    • Accessed September 11, 2017
    • Himmelstein D, Protein SC. 2015j. Protein (target, carrier, transporter, and enzyme) interactions in DrugBank. ThinkLab. https://doi.org/10.15363/thinklab.d65 [Accessed September 11, 2017].
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  • 96
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    • Calculating genomic windows for GWAS lead SNPs
    • Accessed September 11, 2017
    • Himmelstein D, Sirota M, Way G. 2015v. Calculating genomic windows for GWAS lead SNPs. ThinkLab. https://doi.org/10.15363/thinklab.d71 [Accessed September 11, 2017].
    • (2015) Thinklab
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  • 97
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    • Incorporating DrugCentral data in our network
    • Accessed September 11, 2017
    • Himmelstein D, Ursu O, Gilson M, Khankhanian P, Oprea T. 2016d. Incorporating DrugCentral data in our network. ThinkLab. https://doi.org/10.15363/thinklab.d186 [Accessed September 11, 2017].
    • (2016) Thinklab
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  • 98
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    • Incomplete Interactome licensing
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    • Unifying drug vocabularies
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    • Extracting side effects from SIDER 4
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    • MSigDB licensing
    • Accessed September 11, 2017
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    • Disease Ontology feature requests
    • Accessed September 11, 2017
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  • 103
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    • Janet piñero. Processing DisGeNET for disease-gene relationships
    • Accessed September 11, 2017
    • Himmelstein D. 2015f. janet piñero. Processing DisGeNET for disease-gene relationships. ThinkLab. https://doi. org/10.15363/thinklab.d105 [Accessed September 11, 2017].
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  • 104
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    • Functional disease annotations for genes using DOAF
    • Accessed September 11, 2017
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  • 105
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    • Extracting disease-gene associations from the GWAS Catalog
    • Accessed September 11, 2017
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    • Disease similarity from MEDLINE topic co-occurrence
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  • 107
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    • Extracting indications from the ehrlink resource
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    • LINCS L1000 licensing
    • Accessed September 11, 2017
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    • Permuting hetnets and implementing randomized edge swaps in cypher
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    • Using the neo4j graph database for hetnets
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    • Assessing the informativeness of features
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    • Announcing PharmacotherapyDB: The Open Catalog of Drug Therapies for Disease
    • Accessed September 11, 2017
    • Himmelstein D. 2016a. Announcing PharmacotherapyDB: the Open Catalog of Drug Therapies for Disease. ThinkLab. https://doi.org/10.15363/thinklab.d182 [Accessed September 11, 2017].
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    • Assessing the effectiveness of our hetnet permutations
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  • 114
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    • Assessing the imputation quality of gene expression in LINCS L1000
    • Accessed September 11, 2017
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  • 115
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    • Cataloging drug-disease therapies in the ClinicalTrials.Gov database
    • Accessed September 11, 2017
    • Himmelstein D. 2016d. Cataloging drug-disease therapies in the ClinicalTrials.gov database. ThinkLab. https://doi.org/10.15363/thinklab.d212 [Accessed September 11, 2017].
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    • Decomposing predictions into their network support
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    • Decomposing the DWPC to assess intermediate node or edge contributions
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    • Dhimmel/hetio v0.2.0: Neo4j export, Cypher query creation, hetnet stats, and other enhancements
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    • Edge dropout contamination in hetnet edge prediction
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    • Hosting Hetionet in the cloud: Creating a public Neo4j instance
    • Accessed September 11, 2017
    • Himmelstein D. 2016i. Hosting Hetionet in the cloud: creating a public Neo4j instance. ThinkLab. https://doi.org/10.15363/thinklab.d216 [Accessed September 11, 2017].
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  • 121
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    • Exploring the power of Hetionet: A Cypher query depot
    • Accessed September 11, 2017
    • Himmelstein D. 2016j. Exploring the power of Hetionet: a Cypher query depot. ThinkLab. https://doi.org/10.15363/thinklab.d220 [Accessed September 11, 2017].
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    • Our hetnet edge prediction methodology: The modeling framework for Project Rephetio
    • Accessed September 11, 2017
    • Himmelstein D. 2016k. Our hetnet edge prediction methodology: the modeling framework for Project Rephetio. ThinkLab. https://doi.org/10.15363/thinklab.d210 [Accessed September 11, 2017].
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    • Dhimmel/Hetionet V1.0.0: Hetionet V1.0 In Json, Tsv, And Neo4J Formats
    • Himmelstein D. 2017a. Dhimmel/Hetionet V1.0.0: Hetionet V1.0 In Json, Tsv, And Neo4J Formats. Zenodo. https://doi.org/10.5281/zenodo.268568
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    • Dhimmel/Learn V1.0: The Machine Learning Repository For Project Rephetio
    • Himmelstein D. 2017b. Dhimmel/Learn V1.0: The Machine Learning Repository For Project Rephetio. Zenodo. https://doi.org/10.5281/zenodo.268654
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  • 125
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    • Why we predicted ictogenic tricyclic compounds treat epilepsy?
    • Accessed September 11, 2017
    • Himmelstein D. 2017d. Why we predicted ictogenic tricyclic compounds treat epilepsy? ThinkLab. https://doi. org/10.15363/thinklab.d231 [Accessed September 11, 2017].
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  • 126
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    • Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
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    • Himmelstein DS, Baranzini SE. 2015a. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes. PLOS Computational Biology 11:e1004259. DOI: https://doi.org/10.1371/journal.pcbi.1004259, PMID: 26158728
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  • 127
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    • Dhimmel/Gwas-Catalog V1.0: Extracting Gene-Disease Associations From The Gwas Catalog
    • Himmelstein DS, Baranzini SE. 2016b. Dhimmel/Gwas-Catalog V1.0: Extracting Gene-Disease Associations From The Gwas Catalog. Zenodo. https://doi.org/10.15363/thinklab.d80
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  • 128
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    • Dhimmel/Ppi V1.0: Compiling A Human Protein Interaction Catalog
    • Himmelstein DS, Baranzini SE. 2016e. Dhimmel/Ppi V1.0: Compiling A Human Protein Interaction Catalog. Zenodo. DOI: https://doi.org/10.5281/zenodo.48443
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  • 129
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    • Dhimmel/Diseases V1.0: Processing The Diseases Database Of Gene-Disease Associations
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  • 130
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    • Dhimmel/Indications V1.0. Pharmacotherapydb: The Open Catalog Of Drug Therapies For Disease
    • Himmelstein DS, Khankhanian P, Hessler CS, Green AJ, Baranzini SE. 2016q. Dhimmel/Indications V1.0. Pharmacotherapydb: The Open Catalog Of Drug Therapies For Disease. Zenodo. DOI: https://doi.org/10.5281/zenodo.47664
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.