Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
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{"title"=>"Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses", "type"=>"journal", "authors"=>[{"first_name"=>"U. Martin", "last_name"=>"Singh-Blom", "scopus_author_id"=>"55673548400"}, {"first_name"=>"Nagarajan", "last_name"=>"Natarajan", "scopus_author_id"=>"7004412700"}, {"first_name"=>"Ambuj", "last_name"=>"Tewari", "scopus_author_id"=>"8847303800"}, {"first_name"=>"John O.", "last_name"=>"Woods", "scopus_author_id"=>"33768296400"}, {"first_name"=>"Inderjit S.", "last_name"=>"Dhillon", "scopus_author_id"=>"6603876297"}, {"first_name"=>"Edward M.", "last_name"=>"Marcotte", "scopus_author_id"=>"7003412942"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84877027398", "doi"=>"10.1371/journal.pone.0058977", "pui"=>"368847504", "pmid"=>"23650495", "scopus"=>"2-s2.0-84877027398", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"150dd06b-08e5-3d51-a4eb-90b1e17465ca", "abstract"=>"Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall [corrected].", "link"=>"http://www.mendeley.com/research/prediction-validation-genedisease-associations-using-methods-inspired-social-network-analyses", "reader_count"=>100, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>11, "Researcher"=>20, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>38, "Student > Postgraduate"=>5, "Student > Master"=>10, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>11, "Researcher"=>20, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>38, "Student > Postgraduate"=>5, "Student > Master"=>10, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Agricultural and Biological Sciences"=>34, "Business, Management and Accounting"=>2, "Chemistry"=>1, "Computer Science"=>29, "Decision Sciences"=>1, "Engineering"=>4, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>10, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Physics and Astronomy"=>2, "Psychology"=>1, "Social Sciences"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>5}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>34}, "Computer Science"=>{"Computer Science"=>29}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>10}}, "reader_count_by_country"=>{"Canada"=>1, "Korea (South)"=>1, "United States"=>4, "Japan"=>2, "Brazil"=>2, "United Kingdom"=>2, "Slovenia"=>1, "Spain"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1050849"], "description"=>"<p>Left panel corresponds to evaluation of OMIM phenotypes, and the right corresponds to drug data. The vertical axis shows the probability that a true gene association is retrieved in the top- predictions for a disease. The Katz and Catapult methods use all species information, and all the methods use the <b>HumanNet</b> gene network. PRINCE and RWRH are implemented as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Vanunu1\" target=\"_blank\">[7]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Li1\" target=\"_blank\">[8]</a> respectively, but using the <b>HumanNet</b> gene network. The ProDiGe method is implemented as discussed in the Methods section. We have not included the degree based list from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g004\" target=\"_blank\">Figure 4</a>, since all the singleton genes are always given degree 0 during cross-validation. Catapult (solid red) does much better than ProDiGe (the only other supervised method) but does worse compared to walk-based methods than in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g004\" target=\"_blank\">Figure 4</a> (that uses the same setting for all the methods). PRINCE and ProDiGe are consistent with (and sometimes perform slightly better than) the full cross-validation evaluation. RWRH and the Katz measure perform better than the supervised learning methods ProDiGe and Catapult in this evaluation scheme. The fact that PRINCE performs so well on singletons in the drug data case is surprising, given that the only information it uses is the HumanNet gene network.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "cumulative", "withheld", "singleton"], "article_id"=>695680, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g006", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_cumulative_distribution_function_for_the_rank_of_withheld_singleton_genes_/695680", "title"=>"Empirical cumulative distribution function for the rank of withheld singleton genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050858"], "description"=>"<p>Different species used for inferring gene-phenotype associations in the proposed methods Katz and Catapult, and sizes of the gene-phenotype networks for the species, restricted to orthologs of human genes. The total number of human genes with any kind of phenotype annotation is 12331.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease"], "article_id"=>695689, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Species_used_/695689", "title"=>"Species used.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050843"], "description"=>"<p>Left panel corresponds to evaluation of OMIM phenotypes, and the right corresponds to drug data. The vertical axis shows the probability that a true gene association is retrieved in the top- predictions for a disease. Katz and Catapult methods use all species information, and the <b>HumanNet</b> gene network. PRINCE and RWRH methods are implemented as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Vanunu1\" target=\"_blank\">[7]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Li1\" target=\"_blank\">[8]</a> respectively, using the <b>HPRD</b> gene network. ProDiGe method is implemented as discussed in Methods section. Catapult (solid red) does much better across the data sets under this evaluation scheme. In general, the methods get high precision rates in case of the drug data. PRINCE method that does not allow walks through species phenotypes, and OMIM phenotypes in particular, performs much worse than other random-walk based methods. ProDiGe allows sharing of information between phenotypes using the similarities between OMIM phenotypes and performs reasonably well, whereas there is no such sharing possible in case of the drug data due to the absence of drug similarities. The simple degree-based method performs poorly in general. ProDiGe and PRINCE essentially use only the gene network information in case of the drug data.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "cumulative", "withheld"], "article_id"=>695674, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g003", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_cumulative_distribution_function_for_the_rank_of_the_withheld_gene_under_cross_validation_/695674", "title"=>"Empirical cumulative distribution function for the rank of the withheld gene under cross-validation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050856"], "description"=>"<p>Weights learned for different features by Catapult using the biased SVM with bagging procedure, using the HumanNet gene network. Two important observations are: (1) Features corresponding to longer path lengths receive relatively much smaller weights (note that path length can be deduced from the number of terms in the feature, for example, has path length 3, while has path length 4). (2) Features corresponding to different species receive different weights, in particular, features derived from mouse phenotypes get the highest weights, which makes sense given the relative evolutionary proximity between humans and mice. The relative weights of different information sources are not straightforward to interpret. However, we can see that some higher order features are informative.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease"], "article_id"=>695687, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.t005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Weights_by_C_atapult_/695687", "title"=>"Weights by Catapult.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050857"], "description"=>"<p>Top 10 predictions not in the training set by Katz for the same eight OMIM phenotypes as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-t001\" target=\"_blank\">Table 1</a>. Any gene which is among the top 10 candidates for more than one disease is marked in bold. The Katz method shows a weaker link between the number of diseases previously associated with a gene and its presence in the list, while still giving a number of very likely candidates.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "predictions", "katz"], "article_id"=>695688, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.t002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_predictions_for_the_Katz_measure_/695688", "title"=>"Top predictions for the Katz measure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050855"], "description"=>"<p>Benchmark Drug data sets used for evaluation.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "sets"], "article_id"=>695686, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.t004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmark_Drug_data_sets_used_for_evaluation_/695686", "title"=>"Benchmark Drug data sets used for evaluation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050852"], "description"=>"<p>The vertical axis shows the probability that a true gene association is retrieved in the top- predictions for a disease. The Katz and Catapult methods use all species information. PRINCE and RWRH are implemented as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Vanunu1\" target=\"_blank\">[7]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Li1\" target=\"_blank\">[8]</a> respectively, using <b>HPRD</b> network. The ProDiGe method is implemented as discussed in the Methods section. In case of phenotypes with only one known gene (left panel), the only information is the phenotype-phenotype similarity. From the left panel, we note that all network-based methods perform poorly. Nonetheless, we observe a gradation in the performances of different methods, and that Catapult does slightly better. All the methods do substantially better on phenotypes with more than one known gene (right panel).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "cumulative", "withheld", "genes", "omim", "phenotypes"], "article_id"=>695683, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g008", "stats"=>{"downloads"=>1, "page_views"=>37, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_cumulative_distribution_function_for_the_rank_of_withheld_genes_from_OMIM_phenotypes_with_single_known_gene_left_panel_and_more_than_one_known_gene_right_panel_/695683", "title"=>"Empirical cumulative distribution function for the rank of withheld genes from OMIM phenotypes with single known gene (left panel) and more than one known gene (right panel).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050851"], "description"=>"<p>The vertical axis shows the probability that a true gene association is retrieved in the top- predictions for a disease. The Katz and Catapult methods use all species information, and all the methods use the <b>HumanNet</b> gene network. PRINCE and RWRH are implemented as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Vanunu1\" target=\"_blank\">[7]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Li1\" target=\"_blank\">[8]</a> respectively but using the <b>HumanNet</b> gene network. The ProDiGe method is implemented as discussed in the Methods section. We observe that Catapult performs the best. RWRH and Katz methods are competitive as well. The results are consistent with our observations from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g004\" target=\"_blank\">Figure 4</a>.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "cumulative", "withheld", "genes", "omim", "restricted", "linkage"], "article_id"=>695682, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g007", "stats"=>{"downloads"=>1, "page_views"=>28, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_cumulative_distribution_function_for_the_rank_of_withheld_genes_from_OMIM_phenotypes_restricted_to_genes_in_a_small_linkage_neighborhood_of_the_withheld_genes_/695682", "title"=>"Empirical cumulative distribution function for the rank of withheld genes from OMIM phenotypes, restricted to genes in a small linkage neighborhood of the withheld genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050846"], "description"=>"<p>Empirical cumulative distribution function for the rank of the withheld gene under cross-validation. Left panel corresponds to evaluation of OMIM phenotypes, and the right corresponds to drug data. The vertical axis shows the probability that a true gene association is retrieved in the top- predictions for a disease. Katz and Catapult methods use all species information, and all the methods use the <b>HumanNet</b> gene network. PRINCE and RWRH methods are implemented as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Vanunu1\" target=\"_blank\">[7]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone.0058977-Li1\" target=\"_blank\">[8]</a> respectively, but using <b>HumanNet</b>. ProDiGe method is implemented as discussed in Methods section. Again, as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g003\" target=\"_blank\">Figure 3</a>, Catapult (solid red) does the best. An important observation to be made from the plots is that PRINCE and RWRH methods perform relatively much better than in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g003\" target=\"_blank\">Figure 3</a>, where HPRD network was used. (Note that there is no change to the ProDiGe, Katz and Catapult methods; they have identical settings as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g003\" target=\"_blank\">Figure 3</a>).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease"], "article_id"=>695677, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_only_using_HumanNet_/695677", "title"=>"Comparison only using HumanNet.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050860"], "description"=>"<div><p>Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a <i>biased</i> support vector machine where the features are derived from walks in a <i>heterogeneous</i> gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall.</p><p>The authors want to thank Jon Laurent and Kris McGary for some of the data used, and Li and Patra for making their code available. Most of Ambuj Tewari's contribution to this work happened while he was a postdoctoral fellow at the University of Texas at Austin.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "validation", "gene-disease", "associations", "methods", "inspired"], "article_id"=>695691, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_and_Validation_of_Gene_Disease_Associations_Using_Methods_Inspired_by_Social_Network_Analyses_/695691", "title"=>"Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050842"], "description"=>"<p>In the figure above, the disease node is connected to the gene node by one walk of length 2 (solid red line) and three walks of length 3 (dotted, dashed and dashdotted red lines). This can be quickly calculated from the adjacency matrix of the graph: If when there is a link between nodes and , and otherwise, the number of paths of length between genes and is . In the example above, and .</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "derived", "constructing", "walks", "kinds"], "article_id"=>695673, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g002", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Katz_features_are_derived_by_constructing_walks_of_different_kinds_on_the_graph_/695673", "title"=>"Katz features are derived by constructing walks of different kinds on the graph.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050859"], "description"=>"<p>Top 10 predictions not in the training set by Catapult for the eight OMIM phenotypes with the highest number of gene associations. Any gene which is among the top 10 candidates for more than one disease is marked in bold. Catapult does make a great number of very reasonable predictions as observed below. For example, it seems quite likely that both insulin receptor (<i>INSR</i>, 3643) and insulin (<i>INS</i>, 3630) should be associated with insulin resistance, and that many growth factor receptors have been associated with various cancers.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "predictions"], "article_id"=>695690, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Top_predictions_for_C_atapult_/695690", "title"=>"Top predictions for Catapult.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-05-01 01:34:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050840"], "description"=>"<p>The local network around the human disease diabetes insipidus and two genes highly ranked by Catapult, <i>AQP1</i> (top ranked candidate) and <i>MYBL2</i> (ranked as number 40). <i>AQP1</i> is ranked higher than <i>MYBL2</i> because there are more paths from diabetes insipidus to <i>AQP1</i> than to <i>MYBL2</i>, both through model organism phenotypes and through the gene--gene network. Only genes and phenotypes that are associated to both diabetes insipidus and the predicted genes <i>AQP1</i> and <i>MYBL2</i> are shown.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease"], "article_id"=>695671, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_combined_network_in_the_neighborhood_of_a_human_disease_/695671", "title"=>"The combined network in the neighborhood of a human disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050853"], "description"=>"<p>The bar corresponding to the genes on which we did the singleton validation is shown in yellow.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "genes", "omim", "diseases", "drugs"], "article_id"=>695684, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g009", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_the_number_of_known_genes_in_OMIM_diseases_left_and_drugs_right_/695684", "title"=>"Distribution of the number of known genes in OMIM diseases (left) and drugs (right).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1050848"], "description"=>"<p>Left panel corresponds to evaluation of OMIM phenotypes, and the right corresponds to drug data. The vertical axis shows the precision rate, i.e. fraction of true positives in the top- predictions. The horizontal axis shows the recall rate, i.e. ratio of true positives recovered in the top- predictions to the total number of positives for a phenotype (or a drug) in the hidden set. The plots show precision-recall values at various thresholds , in the range and the value at a given is averaged over all the phenotypes (drugs). The plots use the same experimental setup as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g004\" target=\"_blank\">Figure 4</a>, and we observe that the comparisons illustrated by precision-recall measure are consistent with the rank cdf measure in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058977#pone-0058977-g004\" target=\"_blank\">Figure 4</a>.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genetic networks", "Genome databases", "systems biology", "genetics", "Gene function", "Gene networks", "Genetics of disease", "curves", "three-fold"], "article_id"=>695679, "categories"=>["Biological Sciences"], "users"=>["U. Martin Singh-Blom", "Nagarajan Natarajan", "Ambuj Tewari", "John O. Woods", "Inderjit S. Dhillon", "Edward M. Marcotte"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058977.g005", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Precision_Recall_curves_for_three_fold_cross_validation_/695679", "title"=>"Precision-Recall curves for three-fold cross validation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-05-01 01:34:39"}

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Relative Metric

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