Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases
Publication Date
September 23, 2010
Journal
PLOS Computational Biology
Authors
Raoul Frijters, Marianne Van Vugt, Ruben Smeets, René Van Schaik, et al
Volume
6
Issue
9
Pages
e1000943
DOI
https://dx.plos.org/10.1371/journal.pcbi.1000943
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000943
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/20885778
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944780
Europe PMC
http://europepmc.org/abstract/MED/20885778
Web of Science
000282372600011
Scopus
78049427383
Mendeley
http://www.mendeley.com/research/literature-mining-discovery-hidden-connections-between-drugs-genes-diseases
Events
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Literature mining for the discovery of hidden connections between drugs, genes and diseases", "type"=>"journal", "authors"=>[{"first_name"=>"Raoul", "last_name"=>"Frijters", "scopus_author_id"=>"23093785700"}, {"first_name"=>"Marianne", "last_name"=>"van Vugt", "scopus_author_id"=>"57196641721"}, {"first_name"=>"Ruben", "last_name"=>"Smeets", "scopus_author_id"=>"7005003611"}, {"first_name"=>"René", "last_name"=>"van Schaik", "scopus_author_id"=>"57188676293"}, {"first_name"=>"Jacob", "last_name"=>"de Vlieg", "scopus_author_id"=>"6602437344"}, {"first_name"=>"Wynand", "last_name"=>"Alkema", "scopus_author_id"=>"6602469917"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"pui"=>"359882376", "sgr"=>"78049427383", "issn"=>"1553734X", "pmid"=>"20885778", "scopus"=>"2-s2.0-78049427383", "doi"=>"10.1371/journal.pcbi.1000943", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)"}, "id"=>"d024d504-eb7a-3a91-8bf2-6c378729046c", "abstract"=>"The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.", "link"=>"http://www.mendeley.com/research/literature-mining-discovery-hidden-connections-between-drugs-genes-diseases", "reader_count"=>196, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>18, "Librarian"=>6, "Researcher"=>55, "Student > Doctoral Student"=>5, "Student > Ph. D. 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Student"=>58, "Student > Postgraduate"=>6, "Student > Master"=>24, "Other"=>9, "Student > Bachelor"=>7, "Lecturer"=>3, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>67, "Arts and Humanities"=>1, "Philosophy"=>1, "Business, Management and Accounting"=>3, "Chemical Engineering"=>1, "Chemistry"=>7, "Computer Science"=>58, "Engineering"=>1, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>9, "Mathematics"=>2, "Medicine and Dentistry"=>24, "Neuroscience"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Psychology"=>3, "Social Sciences"=>2, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>7}, "Neuroscience"=>{"Neuroscience"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>67}, "Computer Science"=>{"Computer Science"=>58}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>3}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Hong Kong"=>1, "United States"=>7, "United Kingdom"=>3, "Portugal"=>1, "New Caledonia"=>1, "Spain"=>4, "India"=>2, "Canada"=>1, "Netherlands"=>4, "Turkey"=>1, "Korea (South)"=>1, "Finland"=>1, "Mexico"=>1, "Slovenia"=>1, "France"=>2, "Australia"=>1, "Germany"=>2}, "group_count"=>11}

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Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/827911"], "description"=>"<p>A literature-partitioning analysis was performed to evaluate CoPub Discovery's ability to filter true positive (TP) from false positive (FP) hidden relationships in literature. Abstracts published in the year 2000 up to May 1, 2007 were used to validate relationships inferred from abstracts published before the year 2000.</p>", "links"=>[], "tags"=>["validation"], "article_id"=>498274, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CoPub_Discovery_validation_by_literature_partitioning_/498274", "title"=>"CoPub Discovery validation by literature-partitioning.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:17:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/827975"], "description"=>"<p>Receiver Operating Characteristic (ROC) curve analyses were performed to statistically evaluate the ability of CoPub Discovery to distinguish true positive (TP) from false positive (FP) hidden relationships in literature. In this figure, ROC curves are shown of gene-disease, drug-disease and drug-biological process hidden relationship analyses for several intermediate inclusion criteria. For each tested setting, the false positive rate was plotted against the true positive rate for each inferred <i>R</i>-scaled (<i>R</i><sub>i</sub>) score cutoff.</p>", "links"=>[], "tags"=>["computational biology/literature analysis", "molecular biology/bioinformatics", "pharmacology/drug development"], "article_id"=>498342, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CoPub_Discovery_statistical_evaluation_/498342", "title"=>"CoPub Discovery statistical evaluation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:19:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/827841"], "description"=>"<p>Hidden relationships in literature between biomedical concepts (e.g., genes, diseases, drugs), for which A and C have no direct relationship, but are connected indirectly via B-intermediates, can be analyzed in a closed discovery or open discovery setting. The inferred <i>R</i>-scaled (<i>R</i><sub>i</sub>) score between A and C, which is a measure for the strength of a hidden relationship, is calculated by summation of the <i>R</i>-scaled scores of the weakest links (i.e. lowest <i>R</i>-scaled score), divided by the number of intermediates.</p>", "links"=>[], "tags"=>["relationships"], "article_id"=>498205, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ABC_principle_of_hidden_relationships_in_literature_/498205", "title"=>"ABC-principle of hidden relationships in literature.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:16:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/828150"], "description"=>"<p>Hidden relationships are visualized between a) Graves' disease and <i>Programmed cell death 1</i> (PDCD1), b) milnacipran and obsessive-compulsive disorder, and c) pitavastatin and monocyte activation. A and C biomedical concepts are represented as red circles (genes) or red squares (disease, drug or biological process), whereas B-intermediates are represented as blue circles (genes) and orange squares (pathways and biological processes). The edges between nodes represent co-publications in Medline abstracts.</p>", "links"=>[], "tags"=>["computational biology/literature analysis", "molecular biology/bioinformatics", "pharmacology/drug development"], "article_id"=>498513, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Novel_predicted_relationships_/498513", "title"=>"Novel predicted relationships.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:21:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/828278"], "description"=>"<p>The following intermediate inclusion criteria were used to calculate the hidden relationships: minimal number of co-publications between keywords: at least 3; minimal <i>R</i>-scaled score between keywords: at least 20; intermediates used: genes (A and B), and genes, pathways and biological processes (C); literature: till May 1, 2007. The significance cutoff of <i>R</i><sub>i</sub> scores were set to a maximum false-positive rate of 0.1, which was set to 34 for disease-gene and drug-disease relationships, and set to 36 for drug-biological processes hidden relationships, as calculated in the literature partitioning analysis (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000943#pcbi-1000943-g002\" target=\"_blank\">Figure 2</a>).</p>", "links"=>[], "tags"=>["relationships", "biomedical", "concepts"], "article_id"=>498643, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.t001", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_novel_relationships_between_biomedical_concepts_using_the_open_discovery_setting_/498643", "title"=>"Prediction of novel relationships between biomedical concepts using the open discovery setting.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 02:24:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/828313"], "description"=>"<p>The following intermediate inclusion criteria were used to calculate the hidden relationships: minimal number of co-publications between keywords: at least 2; minimal <i>R</i>-scaled score between keywords: at least 20; intermediates used: genes; literature: till May 1<sup>st</sup> 2007. The significance cutoff of <i>R</i><sub>i</sub> scores were set to a maximum false-positive rate of 0.1, which was set to 36 for the used settings, as calculated in the literature partitioning analysis (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000943#pcbi-1000943-g002\" target=\"_blank\">Figure 2</a>).</p>", "links"=>[], "tags"=>["links", "proliferation", "drugs"], "article_id"=>498676, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_novel_links_between_cell_proliferation_and_drugs_using_open_discovery_/498676", "title"=>"Prediction of novel links between cell proliferation and drugs using open discovery.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 02:24:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/412398"], "description"=>"<div><p>The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an <em>in vitro</em> cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.</p></div>", "links"=>[], "tags"=>["mining", "connections", "genes", "diseases"], "article_id"=>141462, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Literature_Mining_for_the_Discovery_of_Hidden_Connections_between_Drugs_Genes_and_Diseases/141462", "title"=>"Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 00:24:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/828223"], "description"=>"<p>The predicted influence of damnacanthal (red line and bullets) and dephostatin (blue line and triangles) on cell proliferation was tested in an <i>in vitro</i> cell assay. For both damnacanthal and dephostatin, the percentage of inhibition was measured and plotted against compound concentration. Both compounds were shown to inhibit cell proliferation in PBMCs when using concentrations in the 1 to 10 µM-range. The EC<sub>50</sub> was estimated at 1.96 µM for dephostatin and for damnacanthal at 2.67 µM.</p>", "links"=>[], "tags"=>["proliferation", "assay", "validates", "copub"], "article_id"=>498588, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g006", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_vitro_cell_proliferation_assay_validates_CoPub_Discovery_s_prediction_/498588", "title"=>"<i>In vitro</i> cell proliferation assay validates CoPub Discovery's prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:23:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/828066"], "description"=>"<p>The time lag between the data needed for prediction of a hidden relationship and its actual assertion in literature is plotted for 1000 hidden relationships. For each hidden relationship, the average publication date of all A–B and B–C literature appearances was calculated and compared with the date on which A and C were first mentioned in the literature. Note: the data was derived from the literature-partitioning analysis.</p>", "links"=>[], "tags"=>["lag", "relationships"], "article_id"=>498429, "categories"=>["Pharmacology", "Molecular Biology", "Cancer"], "users"=>["Raoul Frijters", "Marianne van Vugt", "Ruben Smeets", "René van Schaik", "Jacob de Vlieg", "Wynand Alkema"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000943.g004", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_lag_between_prediction_and_first_appearance_of_hidden_relationships_in_literature_/498429", "title"=>"Time lag between prediction and first appearance of hidden relationships in literature.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:20:29"}

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