Self-Correcting Maps of Molecular Pathways
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{"title"=>"Self-correcting maps of molecular pathways", "type"=>"journal", "authors"=>[{"first_name"=>"Andrey", "last_name"=>"Rzhetsky", "scopus_author_id"=>"35465167600"}, {"first_name"=>"Tian", "last_name"=>"Zheng", "scopus_author_id"=>"7201358841"}, {"first_name"=>"Chani", "last_name"=>"Weinreb", "scopus_author_id"=>"8844079400"}], "year"=>2006, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"46849085966", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "issn"=>"19326203", "pmid"=>"17183692", "pui"=>"352571191", "doi"=>"10.1371/journal.pone.0000061", "scopus"=>"2-s2.0-46849085966"}, "id"=>"dfe989ad-a94e-392a-84c5-dc55da870e12", "abstract"=>"Reliable and comprehensive maps of molecular pathways are indispensable for guiding complex biomedical experiments. Such maps are typically assembled from myriads of disparate research reports and are replete with inconsistencies due to variations in experimental conditions and/or errors. It is often an intractable task to manually verify internal consistency over a large collection of experimental statements. To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables. We show how to obtain a non-contradictory model of a molecular network by computing the joint distribution for arc and node variables, and then apply our methodology to a realistic network, generating a set of experimentally testable hypotheses. This network, derived from an automated analysis of over 3,000 full-text research articles, includes genes that have been hypothetically linked to four neurological disorders: Alzheimer's disease, autism, bipolar disorder, and schizophrenia. We estimated that approximately 10% of the published molecular interactions are logically incompatible. Our approach can be directly applied to an array of diverse problems including those encountered in molecular biology, ecology, economics, politics, and sociology.", "link"=>"http://www.mendeley.com/research/selfcorrecting-maps-molecular-pathways", "reader_count"=>31, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>11, "Student > Ph. D. Student"=>9, "Student > Master"=>2, "Other"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>11, "Student > Ph. D. Student"=>9, "Student > Master"=>2, "Other"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Mathematics"=>3, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>1, "Neuroscience"=>1, "Psychology"=>3, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Belgium"=>1, "United States"=>4, "Denmark"=>1, "United Kingdom"=>1}, "group_count"=>2}

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  • {"files"=>["https://ndownloader.figshare.com/files/954746"], "description"=>"<div><p>(A) The absolute difference between the reconciled (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000061#pone-0000061-g002\" target=\"_blank\">Figure 2</a> B) and the prior (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000061#pone-0000061-g002\" target=\"_blank\">Figure 2</a> A) distributions.</p>\n <p>For the <i>activate</i> arc value, an increase in probability is shown in yellow; a decrease is shown in red.</p>\n <p>Similarly, for the <i>inhibit</i> arc value, an increase in probability is shown in blue; a decrease is shown in green.</p>\n <p>For graph nodes, positive changes (increases) in the probability of observing the node in the <i>active/present</i> state are shown in red; negative changes (decreases) are shown in blue.</p>\n <p>(B) Differences in the Shannon entropy (bits) for arc and node variables between the reconciled and prior marginal distributions.</p>\n <p>Red variables lost their entropy (gained information), whereas blue ones increased their entropy (lost information), after computation of reconciled distributions.</p>\n <p>The nodes that we mentioned in the text have the following coordinates: <i>WNT1 (6b), HBP1 (6b), EMX2 (6b), SRF (3b), SP1 (3b), TP53 (4b), PSEN1 (5c)</i>.</p></div>", "links"=>[], "tags"=>["entropy-change", "graphs", "networks"], "article_id"=>625040, "categories"=>["Biological Sciences", "Cell Biology"], "users"=>["Andrey Rzhetsky", "Tian Zheng", "Chani Weinreb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000061.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Figure_3_Difference_and_entropy_change_graphs_for_networks_shown_in_Figure_2_/625040", "title"=>"Figure 3. Difference and entropy-change graphs for networks shown in Figure 2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:00:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/954526"], "description"=>"<div><p>A node in the network is a random variable that can have one of two values, <i>false</i> or <i>true</i> (0 or 1, respectively).</p>\n <p>Both the brightness and the size of a node represent the strength of the probability that the corresponding molecule is <i>present/active</i> in the tissue or cell of interest, <i>P</i>(<i>Vi</i> = 1).</p>\n <p>A higher probability is depicted with a lighter color and larger ball radius (see key to the node color and size); when the <i>P</i>(<i>Vi</i> = 1) drops to 0, the node disappears from the figure (the ball radius drops to zero).</p>\n <p>Each arc is a random variable with three possible different values: <i>inhibit, activate</i>, and <i>no effect</i> (<i>−1</i>, 1, and 0, respectively).</p>\n <p>Complete confidence that an arc <i>A<sub>V,U</sub></i> represents an inhibiting function (<i>P</i>(<i>A<sub>V,U</sub></i> = −1) = 1) would be drawn as a thick bright-blue edge with a disk at the end (the leftmost edge in the key to the figure).</p>\n <p>If both probabilities (<i>P</i>(<i>A<sub>V,U</sub></i> = −1) and <i>P</i>(<i>A<sub>V,U</sub></i> = 1)) drop to zero (indicating that <i>P</i>(<i>A<sub>V,U</sub></i> = 0) = 1), then the edge vanishes from the figure, indicating the <i>no effect</i> value.</p>\n <p>(A) An internally consistent set of prior probabilities.</p>\n <p>The resulting marginal distributions are either unchanged (on the input nodes G and B and on the sink node E) or have a decreased entropy (on all arcs and on nodes C and D), in contrast to the prior probabilities.</p>\n <p>(B) An example with inconsistent prior probabilities.</p>\n <p>The marginal distribution for arc <i>A<sub>BC</sub></i> is reversed with respect to the prior.</p>\n <p>(C) Another example of conflicting prior probabilities. Here, node C changed its distribution significantly.</p></div>", "links"=>[], "tags"=>["marginal", "distributions", "variables", "hypothetical"], "article_id"=>624817, "categories"=>["Biological Sciences", "Cell Biology"], "users"=>["Andrey Rzhetsky", "Tian Zheng", "Chani Weinreb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000061.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computation_of_marginal_distributions_for_all_variables_arcs_and_nodes_of_a_hypothetical_toy_graph_/624817", "title"=>"Computation of marginal distributions for all variables (arcs and nodes) of a hypothetical toy graph.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:59:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/954629"], "description"=>"<div><p>(A) Prior distributions for arcs and nodes computed by automated analysis of thousands of research articles.</p>\n <p>(B) Reconciled marginal distributions for all variables in the graph: The graph has changed to improve the consistency of individual pieces of information, some of which were conflicting in the graph A.</p>\n <p>Green, blue, yellow, and red nodes correspond to genes that were previously reported as associated with Alzheimer's disease, autism, bipolar disorder, and schizophrenia, respectively.</p>\n <p>The nodes that we mentioned in the text have the following coordinates: <i>WNT1 (6b), HBP1 (6b), EMX2 (6b), SRF (3b), SP1 (3b), TP53 (4b), PSEN1 (5c)</i>.</p></div>", "links"=>[], "tags"=>["arc", "node", "variables", "molecular"], "article_id"=>624918, "categories"=>["Biological Sciences", "Cell Biology"], "users"=>["Andrey Rzhetsky", "Tian Zheng", "Chani Weinreb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000061.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_for_all_arc_and_node_variables_in_a_large_human_molecular_network_/624918", "title"=>"Distributions for all arc and node variables in a large human molecular network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 13:00:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/470138", "https://ndownloader.figshare.com/files/470209"], "description"=>"<div><p>Reliable and comprehensive maps of molecular pathways are indispensable for guiding complex biomedical experiments. Such maps are typically assembled from myriads of disparate research reports and are replete with inconsistencies due to variations in experimental conditions and/or errors. It is often an intractable task to manually verify internal consistency over a large collection of experimental statements. To automate large-scale reconciliation efforts, we propose a random-arcs-and-nodes model where both nodes (tissue-specific states of biological molecules) and arcs (interactions between them) are represented with random variables. We show how to obtain a non-contradictory model of a molecular network by computing the joint distribution for arc and node variables, and then apply our methodology to a realistic network, generating a set of experimentally testable hypotheses. This network, derived from an automated analysis of over 3,000 full-text research articles, includes genes that have been hypothetically linked to four neurological disorders: Alzheimer's disease, autism, bipolar disorder, and schizophrenia. We estimated that approximately 10% of the published molecular interactions are logically incompatible. Our approach can be directly applied to an array of diverse problems including those encountered in molecular biology, ecology, economics, politics, and sociology.</p> </div>", "links"=>[], "tags"=>["self-correcting", "maps", "molecular", "pathways"], "article_id"=>152663, "categories"=>["Biological Sciences", "Cell Biology"], "users"=>["Andrey Rzhetsky", "Tian Zheng", "Chani Weinreb"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000061.s001", "https://dx.doi.org/10.1371/journal.pone.0000061.s002"], "stats"=>{"downloads"=>4, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Self_Correcting_Maps_of_Molecular_Pathways/152663", "title"=>"Self-Correcting Maps of Molecular Pathways", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2006-12-20 00:44:23"}

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Relative Metric

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