De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae
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{"title"=>"De-novo learning of genome-scale regulatory networks in S. cerevisiae", "type"=>"journal", "authors"=>[{"first_name"=>"Sisi", "last_name"=>"Ma", "scopus_author_id"=>"34870043500"}, {"first_name"=>"Patrick", "last_name"=>"Kemmeren", "scopus_author_id"=>"6603647513"}, {"first_name"=>"David", "last_name"=>"Gresham", "scopus_author_id"=>"12799271400"}, {"first_name"=>"Alexander", "last_name"=>"Statnikov", "scopus_author_id"=>"10241774000"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25215507", "doi"=>"10.1371/journal.pone.0106479", "pui"=>"608758647", "issn"=>"19326203", "sgr"=>"84929939543", "scopus"=>"2-s2.0-84929939543"}, "id"=>"23b0524a-ec63-381d-86d2-fe0269cd13fa", "abstract"=>"De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in Saccharomyces cerevisiae that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of S. cerevisiae gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.", "link"=>"http://www.mendeley.com/research/denovo-learning-genomescale-regulatory-networks-s-cerevisiae", "reader_count"=>26, "reader_count_by_academic_status"=>{"Librarian"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Librarian"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>1, "Student > Master"=>4, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>12, "Medicine and Dentistry"=>2, "Chemical Engineering"=>1, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"United States"=>3, "Portugal"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1674632"], "description"=>"<p>Inhibiting edges are shown with red, and excitatory edges are shown with black.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169152, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Direct_regulatory_interactions_between_transcription_factors_in_gold_standard_gene_regulatory_network_1_/1169152", "title"=>"Direct regulatory interactions between transcription factors in gold-standard gene regulatory network #1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674637"], "description"=>"<p>The analysis was performed in Cytoscape with NetworkAnalyzer.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169156, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Topological_analysis_of_gold_standard_gene_regulatory_network_1_/1169156", "title"=>"Topological analysis of gold-standard gene regulatory network #1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674610"], "description"=>"<p>The relations in constructed gene regulatory network correspond to direct regulatory interactions.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169132, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Construction_of_gene_regulatory_networks_by_integrating_targeted_perturbation_data_with_binding_data_/1169132", "title"=>"Construction of gene regulatory networks by integrating targeted perturbation data with binding data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674658"], "description"=>"<p>ROC curves of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to datasets of each type.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169174, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g006", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_of_the_Pareto_frontier_for_sensitivity_specificity_pairs_obtained_by_application_of_18_network_reverse_engineering_approaches_to_datasets_of_each_type_/1169174", "title"=>"ROC curves of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to datasets of each type.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674666"], "description"=>"<p>The left panel shows the scatter-plot and the right panel shows the null distribution for establishing statistical significance of the observed correlation.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169182, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g007", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_scatter_plot_of_transcription_factor_connectivity_versus_the_accuracy_combined_PPV_NPV_metric_of_reconstructing_their_sub_networks_/1169182", "title"=>"Example scatter-plot of transcription factor connectivity versus the accuracy (combined PPV/NPV metric) of reconstructing their sub-networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674667"], "description"=>"<p>Assessment of currently available genome-scale gold-standard networks used by prior gene network reverse-engineering studies.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169183, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t001", "stats"=>{"downloads"=>4, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Assessment_of_currently_available_genome_scale_gold_standard_networks_used_by_prior_gene_network_reverse_engineering_studies_/1169183", "title"=>"Assessment of currently available genome-scale gold-standard networks used by prior gene network reverse-engineering studies.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674668"], "description"=>"<p>Overlapping identified binding with regulatory relations results in gold-standard networks with direct regulatory relations.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169184, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlapping_identified_binding_with_regulatory_relations_results_in_gold_standard_networks_with_direct_regulatory_relations_/1169184", "title"=>"Overlapping identified binding with regulatory relations results in gold-standard networks with direct regulatory relations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674669"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s008\" target=\"_blank\">Table S1</a></b> part A for a colored version of this table.</p><p>Sensitivity and specificity.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169186, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sensitivity_and_specificity_/1169186", "title"=>"Sensitivity and specificity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674671"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s008\" target=\"_blank\">Table S1</a></b> part B for a colored version of this table.</p><p>Euclidean distance from the optimal algorithm with sensitivity  = 1 and specificity  = 1.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169187, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t004", "stats"=>{"downloads"=>11, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Euclidean_distance_from_the_optimal_algorithm_with_sensitivity_8202_8202_1_and_specificity_8202_8202_1_/1169187", "title"=>"Euclidean distance from the optimal algorithm with sensitivity  = 1 and specificity  = 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674672"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s009\" target=\"_blank\">Table S2</a></b> part A for a colored version of this table.</p><p>Positive predictive value (PPV) and negative predictive value (NPV).</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169188, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t005", "stats"=>{"downloads"=>5, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Positive_predictive_value_PPV_and_negative_predictive_value_NPV_/1169188", "title"=>"Positive predictive value (PPV) and negative predictive value (NPV).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674674"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s009\" target=\"_blank\">Table S2</a></b> part B for a colored version of this table.</p><p>Euclidean distance from the optimal algorithm with PPV  = 1 and NPV  = 1.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169190, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t006", "stats"=>{"downloads"=>8, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Euclidean_distance_from_the_optimal_algorithm_with_PPV_8202_8202_1_and_NPV_8202_8202_1_/1169190", "title"=>"Euclidean distance from the optimal algorithm with PPV  = 1 and NPV  = 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674675"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s010\" target=\"_blank\">Table S3</a></b> part A for a colored version of this table.</p><p>Recall (sensitivity) and precision (PPV).</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169191, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t007", "stats"=>{"downloads"=>3, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Recall_sensitivity_and_precision_PPV_/1169191", "title"=>"Recall (sensitivity) and precision (PPV).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674676"], "description"=>"<p>Cells with bold font correspond to experiments with statistically significant reconstruction of regulatory networks. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone-0106479-t011\" target=\"_blank\"><b>Table 11</b></a> for abbreviations of row labels. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479.s010\" target=\"_blank\">Table S3</a></b> part B for a colored version of this table.</p><p>Euclidean distance from the optimal algorithm with Sensitivity = 1 and PPV  = 1.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169192, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t008", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Euclidean_distance_from_the_optimal_algorithm_with_Sensitivity_8202_8202_1_and_PPV_8202_8202_1_/1169192", "title"=>"Euclidean distance from the optimal algorithm with Sensitivity = 1 and PPV  = 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674678"], "description"=>"<p>The correlations were assessed for 13 different networks (derived from 13 gene expression microarray datasets) for each combination of network reverse-engineering approaches and combined accuracy metrics. Statistical significance is assessed at 5% alpha level adjusted globally for multiple comparisons (over all statistical tests performed for the table). The left portion of the table corresponds to transcription factor connectivity assessed in the gold-standard network, and the right portion corresponds to transcription factor connectivity assessed in the inferred network.</p><p>Number of networks that have significant correlations between transcription factor connectivity and accuracy of reconstructing their sub-networks.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169195, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t009", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_networks_that_have_significant_correlations_between_transcription_factor_connectivity_and_accuracy_of_reconstructing_their_sub_networks_/1169195", "title"=>"Number of networks that have significant correlations between transcription factor connectivity and accuracy of reconstructing their sub-networks.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674680"], "description"=>"<p>Datasets used for gene regulatory network reverse-engineering.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169196, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t010", "stats"=>{"downloads"=>14, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Datasets_used_for_gene_regulatory_network_reverse_engineering_/1169196", "title"=>"Datasets used for gene regulatory network reverse-engineering.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674681"], "description"=>"<p>“FDR” refers to thresholding associations at 5% FDR using the methodology of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479-Benjamini1\" target=\"_blank\">[23]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106479#pone.0106479-Benjamini2\" target=\"_blank\">[24]</a>. “Alpha” refers to thresholding associations at 5% alpha. “AND” rule implies that if the algorithm run on X outputs Y <i>and</i> if the algorithm run on Y outputs X, then X and Y have an edge in the resulting network, and (ii) “OR” rule implies that if the algorithm run on X outputs Y <i>or</i> if the algorithm run on Y outputs X, then X and Y have an edge in the resulting network.</p><p>Statistical approaches used for gene regulatory network reverse-engineering.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169197, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.t011", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_approaches_used_for_gene_regulatory_network_reverse_engineering_/1169197", "title"=>"Statistical approaches used for gene regulatory network reverse-engineering.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1675321", "https://ndownloader.figshare.com/files/1675322", "https://ndownloader.figshare.com/files/1675323", "https://ndownloader.figshare.com/files/1675324", "https://ndownloader.figshare.com/files/1675325", "https://ndownloader.figshare.com/files/1675326", "https://ndownloader.figshare.com/files/1675327", "https://ndownloader.figshare.com/files/1675328", "https://ndownloader.figshare.com/files/1675329", "https://ndownloader.figshare.com/files/1675330", "https://ndownloader.figshare.com/files/1675331", "https://ndownloader.figshare.com/files/1675332", "https://ndownloader.figshare.com/files/1675333", "https://ndownloader.figshare.com/files/1675334", "https://ndownloader.figshare.com/files/1675335", "https://ndownloader.figshare.com/files/1675336", "https://ndownloader.figshare.com/files/1675337", "https://ndownloader.figshare.com/files/1675338", "https://ndownloader.figshare.com/files/1675339", "https://ndownloader.figshare.com/files/1675340", "https://ndownloader.figshare.com/files/1675341", "https://ndownloader.figshare.com/files/1675349"], "description"=>"<div><p>De-novo reverse-engineering of genome-scale regulatory networks is a fundamental problem of biological and translational research. One of the major obstacles in developing and evaluating approaches for de-novo gene network reconstruction is the absence of high-quality genome-scale gold-standard networks of direct regulatory interactions. To establish a foundation for assessing the accuracy of de-novo gene network reverse-engineering, we constructed high-quality genome-scale gold-standard networks of direct regulatory interactions in <i>Saccharomyces cerevisiae</i> that incorporate binding and gene knockout data. Then we used 7 performance metrics to assess accuracy of 18 statistical association-based approaches for de-novo network reverse-engineering in 13 different datasets spanning over 4 data types. We found that most reconstructed networks had statistically significant accuracies. We also determined which statistical approaches and datasets/data types lead to networks with better reconstruction accuracies. While we found that de-novo reverse-engineering of the entire network is a challenging problem, it is possible to reconstruct sub-networks around some transcription factors with good accuracy. The latter transcription factors can be identified by assessing their connectivity in the inferred networks. Overall, this study provides the gene network reverse-engineering community with a rigorous assessment of the accuracy of <i>S. cerevisiae</i> gene network reconstruction and variability in performance of various approaches for learning both the entire network and sub-networks around transcription factors.</p></div>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169704, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0106479.s001", "https://dx.doi.org/10.1371/journal.pone.0106479.s002", "https://dx.doi.org/10.1371/journal.pone.0106479.s003", "https://dx.doi.org/10.1371/journal.pone.0106479.s004", "https://dx.doi.org/10.1371/journal.pone.0106479.s005", "https://dx.doi.org/10.1371/journal.pone.0106479.s006", "https://dx.doi.org/10.1371/journal.pone.0106479.s007", "https://dx.doi.org/10.1371/journal.pone.0106479.s008", "https://dx.doi.org/10.1371/journal.pone.0106479.s009", "https://dx.doi.org/10.1371/journal.pone.0106479.s010", "https://dx.doi.org/10.1371/journal.pone.0106479.s011", "https://dx.doi.org/10.1371/journal.pone.0106479.s012", "https://dx.doi.org/10.1371/journal.pone.0106479.s013", "https://dx.doi.org/10.1371/journal.pone.0106479.s014", "https://dx.doi.org/10.1371/journal.pone.0106479.s015", "https://dx.doi.org/10.1371/journal.pone.0106479.s016", "https://dx.doi.org/10.1371/journal.pone.0106479.s017", "https://dx.doi.org/10.1371/journal.pone.0106479.s018", "https://dx.doi.org/10.1371/journal.pone.0106479.s019", "https://dx.doi.org/10.1371/journal.pone.0106479.s020", "https://dx.doi.org/10.1371/journal.pone.0106479.s021", "https://dx.doi.org/10.1371/journal.pone.0106479.s022"], "stats"=>{"downloads"=>87, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_De_Novo_Learning_of_Genome_Scale_Regulatory_Networks_in_S_cerevisiae_/1169704", "title"=>"<i>De-Novo</i> Learning of Genome-Scale Regulatory Networks in <i>S. cerevisiae</i>", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674621"], "description"=>"<p>Transcription factors are shown with large blue circles, and other genes are shown with small green circles. Edges in the network represent direct regulatory interactions. Inhibiting edges are shown with red, and excitatory edges are shown with black.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169142, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gold_standard_gene_regulatory_network_1_/1169142", "title"=>"Gold-standard gene regulatory network #1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1674642"], "description"=>"<p>ROC curve of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to 13 datasets.</p>", "links"=>[], "tags"=>["latter transcription factors", "cerevisiae gene network reconstruction", "4 data types", "transcription factors", "7 performance metrics", "gene knockout data"], "article_id"=>1169161, "categories"=>["Biological Sciences"], "users"=>["Sisi Ma", "Patrick Kemmeren", "David Gresham", "Alexander Statnikov"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0106479.g005", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curve_of_the_Pareto_frontier_for_sensitivity_specificity_pairs_obtained_by_application_of_18_network_reverse_engineering_approaches_to_13_datasets_/1169161", "title"=>"ROC curve of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to 13 datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-09-12 02:45:06"}

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