Graph Theory Enables Drug Repurposing – How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action
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{"title"=>"Graph theory enables drug repurposing - How a mathematical model can drive the discovery of hidden mechanisms of action", "type"=>"journal", "authors"=>[{"first_name"=>"Ruggero", "last_name"=>"Gramatica", "scopus_author_id"=>"54897899100"}, {"first_name"=>"T.", "last_name"=>"Di Matteo", "scopus_author_id"=>"57190392609"}, {"first_name"=>"Stefano", "last_name"=>"Giorgetti", "scopus_author_id"=>"56097430100"}, {"first_name"=>"Massimo", "last_name"=>"Barbiani", "scopus_author_id"=>"56096432600"}, {"first_name"=>"Dorian", "last_name"=>"Bevec", "scopus_author_id"=>"56351787500"}, {"first_name"=>"Tomaso", "last_name"=>"Aste", "scopus_author_id"=>"7003460450"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84897490774", "doi"=>"10.1371/journal.pone.0084912", "pui"=>"372763066", "pmid"=>"24416311", "scopus"=>"2-s2.0-84897490774", "issn"=>"19326203", "arxiv"=>"1306.0924"}, "id"=>"8cada22e-8aec-32e1-a069-75d55877cdad", "abstract"=>"We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.", "link"=>"http://www.mendeley.com/research/graph-theory-enables-drug-repurposing-mathematical-model-drive-discovery-hidden-mechanisms-action", "reader_count"=>69, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>16, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>3, "Other"=>9, "Student > Master"=>9, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>16, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>3, "Other"=>9, "Student > Master"=>9, "Student > Bachelor"=>4, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>15, "Arts and Humanities"=>3, "Chemistry"=>4, "Computer Science"=>18, "Engineering"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>2, "Medicine and Dentistry"=>8, "Neuroscience"=>2, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Psychology"=>1, "Social Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Engineering"=>{"Engineering"=>3}, "Chemistry"=>{"Chemistry"=>4}, "Neuroscience"=>{"Neuroscience"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>18}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"South Korea"=>1, "Netherlands"=>1, "Korea (South)"=>1, "United States"=>1, "United Kingdom"=>1, "Italy"=>1, "Slovenia"=>1, "France"=>2}, "group_count"=>10}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1344832"], "description"=>"<p>(A) Every document is split into its constituent sentences and each of them is scanned to identify expressions registered on the dictionary. In the figure, two sentences are highlighted and the matching expressions are enclosed in coloured boxes. Every one of these expressions is associated to a concept in the dictionary. (B) The concepts co-occurring in a sentence are connected pairwise. A sentence is therefore abstracted as a complete graph where the occurring concepts are the nodes and a single co-occurrence is a link. The weight of a link is increased if more instances of the same co-occurrence are present. (C) The sentence graphs are then merged in such a way that each node (concept) appears only once in the graph. In the figure it is evident that the «LAM» node (abbreviation for Lymphangioleiomyomatosis – a rare disease) appears in every graph and the «Lung» node in two of them. (D) The result of the merging is a new graph – which is no more complete – where the weight of the link is associated to the frequency of the same co-occurrence.</p>", "links"=>[], "tags"=>["biotechnology", "drug discovery", "Computational biology", "text mining", "Applied mathematics", "Complex systems", "Drugs and devices", "Drug research and development", "Drug information", "Statistical mechanics", "information science", "information theory", "linguistics", "Computational linguistics", "graph"], "article_id"=>899069, "categories"=>["Physics", "Medicine", "Information And Computing Sciences", "Mathematics", "Sociology", "Biological Sciences"], "users"=>["Ruggero Gramatica", "T. Di Matteo", "Stefano Giorgetti", "Massimo Barbiani", "Dorian Bevec", "Tomaso Aste"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084912.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Conceptual_outline_of_the_knowledge_graph_building_process_/899069", "title"=>"Conceptual outline of the knowledge graph building process.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-09 04:09:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1344834"], "description"=>"<p>(A) This figure shows a version of the graph – simplified for illustration purposes – built focusing onto 300 concepts and with 200,000 documents. (B) This figure shows three automatically retrieved and meaningful paths, identifying three – out of five – prospect candidate peptides for sarcoidosis. The paths are depicted in a further simplified version of the graph obtained from the first one by filtering out nodes not relevant to the paths.</p>", "links"=>[], "tags"=>["biotechnology", "drug discovery", "Computational biology", "text mining", "Applied mathematics", "Complex systems", "Drugs and devices", "Drug research and development", "Drug information", "Statistical mechanics", "information science", "information theory", "linguistics", "Computational linguistics"], "article_id"=>899071, "categories"=>["Physics", "Medicine", "Information And Computing Sciences", "Mathematics", "Sociology", "Biological Sciences"], "users"=>["Ruggero Gramatica", "T. Di Matteo", "Stefano Giorgetti", "Massimo Barbiani", "Dorian Bevec", "Tomaso Aste"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084912.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Paths_identification_and_selection_/899071", "title"=>"Paths identification and selection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-09 04:09:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1344835"], "description"=>"<p>A portion of the knowledge network showing the neighbourhood of Sarcoidosis. The figure is intended as a bird-eye view of the entities the system detected as related with Sarcoidosis</p>", "links"=>[], "tags"=>["biotechnology", "drug discovery", "Computational biology", "text mining", "Applied mathematics", "Complex systems", "Drugs and devices", "Drug research and development", "Drug information", "Statistical mechanics", "information science", "information theory", "linguistics", "Computational linguistics", "sarcoidosis"], "article_id"=>899072, "categories"=>["Physics", "Medicine", "Information And Computing Sciences", "Mathematics", "Sociology", "Biological Sciences"], "users"=>["Ruggero Gramatica", "T. Di Matteo", "Stefano Giorgetti", "Massimo Barbiani", "Dorian Bevec", "Tomaso Aste"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084912.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Sarcoidosis_knowledge_network_/899072", "title"=>"The Sarcoidosis knowledge network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-09 04:09:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/1344836"], "description"=>"<p>The VIP – SARCOIDOSIS path and other closely related concepts.</p>", "links"=>[], "tags"=>["biotechnology", "drug discovery", "Computational biology", "text mining", "Applied mathematics", "Complex systems", "Drugs and devices", "Drug research and development", "Drug information", "Statistical mechanics", "information science", "information theory", "linguistics", "Computational linguistics", "vip", "sarcoidosis"], "article_id"=>899073, "categories"=>["Physics", "Medicine", "Information And Computing Sciences", "Mathematics", "Sociology", "Biological Sciences"], "users"=>["Ruggero Gramatica", "T. Di Matteo", "Stefano Giorgetti", "Massimo Barbiani", "Dorian Bevec", "Tomaso Aste"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084912.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_VIP_8211_SARCOIDOSIS_path_and_other_closely_related_concepts_/899073", "title"=>"The VIP – SARCOIDOSIS path and other closely related concepts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-09 04:09:09"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1344838"], "description"=>"<p>The CNP – SARCOIDOSIS path and other closely related concepts.</p>", "links"=>[], "tags"=>["biotechnology", "drug discovery", "Computational biology", "text mining", "Applied mathematics", "Complex systems", "Drugs and devices", "Drug research and development", "Drug information", "Statistical mechanics", "information science", "information theory", "linguistics", "Computational linguistics", "cnp", "sarcoidosis"], "article_id"=>899075, "categories"=>["Physics", "Medicine", "Information And Computing Sciences", "Mathematics", "Sociology", "Biological Sciences"], "users"=>["Ruggero Gramatica", "T. Di Matteo", "Stefano Giorgetti", "Massimo Barbiani", "Dorian Bevec", "Tomaso Aste"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084912.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_CNP_8211_SARCOIDOSIS_path_and_other_closely_related_concepts_/899075", "title"=>"The CNP – SARCOIDOSIS path and other closely related concepts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-09 04:09:09"}
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Relative Metric

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