DREAM3: Network Inference Using Dynamic Context Likelihood of Relatedness and the Inferelator
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{"title"=>"DREAM3: Network inference using dynamic context likelihood of relatedness and the inferelator", "type"=>"journal", "authors"=>[{"first_name"=>"Aviv", "last_name"=>"Madar", "scopus_author_id"=>"23094783500"}, {"first_name"=>"Alex", "last_name"=>"Greenfield", "scopus_author_id"=>"35774030000"}, {"first_name"=>"Eric", "last_name"=>"Vanden-Eijnden", "scopus_author_id"=>"55664338700"}, {"first_name"=>"Richard", "last_name"=>"Bonneau", "scopus_author_id"=>"7006793027"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-78149461178", "pui"=>"368261794", "doi"=>"10.1371/journal.pone.0009803", "isbn"=>"0001404105", "sgr"=>"78149461178", "pmid"=>"20339551"}, "id"=>"01a3756f-bca2-349a-b5c8-9afe7cf88692", "abstract"=>"BACKGROUND: Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions.\\n\\nMETHODOLOGY: We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model.\\n\\nCONCLUSIONS/SIGNIFICANCE: Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. \"who regulates who\" (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.", "link"=>"http://www.mendeley.com/research/dream3-network-inference-using-dynamic-context-likelihood-relatedness-inferelator", "reader_count"=>114, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>9, "Researcher"=>36, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>3, "Student > Master"=>16, "Other"=>7, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>9, "Researcher"=>36, "Student > Ph. D. Student"=>36, "Student > Postgraduate"=>3, "Student > Master"=>16, "Other"=>7, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>9, "Mathematics"=>4, "Agricultural and Biological Sciences"=>58, "Medicine and Dentistry"=>3, "Arts and Humanities"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>2, "Computer Science"=>26, "Immunology and Microbiology"=>1, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>58}, "Computer Science"=>{"Computer Science"=>26}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>4}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>4, "Norway"=>1, "China"=>1, "Taiwan"=>1, "United Kingdom"=>3, "France"=>1, "Chile"=>1, "Switzerland"=>1, "Germany"=>2}, "group_count"=>12}

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  • {"files"=>["https://ndownloader.figshare.com/files/858108"], "description"=>"<p>In this table we present a more detailed view of performance for our method's best predicted network ( total regulatory interactions with up to regulators controlling each gene). The table inline method precision [%] at varying degrees of completeness (recall [%]).</p>", "links"=>[], "tags"=>["methods", "precision", "low-to-high"], "article_id"=>528566, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.t001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_2_E_coli_2_Methods_precision_for_low_to_high_recall_values_/528566", "title"=>"Network 2 (E.coli-2): Methods precision for low-to-high recall values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:11:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/857618"], "description"=>"<p>We evaluated the performance of Inferelator 1.0 and three different versions of CLR—namely: original-CLR (CLR), dynamic-CLR, and mixed-CLR—with or without Inferelator 1.0, at three levels of knock-out filtration, . To make DREAM3 predictions we used mixed-CLR with Inferelator 1.0 (with filtration cutoff ), resulting in area-under precision vs. recall curve of (p-value, ), and area-under receiver operating characteristic curve of (p-value, ). We show that the pipeline we used to make DREAM3 predictions produced optimal performance, compared to other tested CLR/Inferelator 1.0 combinations. Error bars for methods involving Inferelator 1.0 (variability due to cross validation) are approximately within of Precision vs. Recall area-under-curve values and are thus not shown.</p>", "links"=>[], "tags"=>["area-under", "precision", "curves", "dream3"], "article_id"=>528072, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_area_under_precision_vs_recall_curves_for_DREAM3_five_gene_networks_/528072", "title"=>"Mean area-under precision vs. recall curves for DREAM3 five -gene networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:08:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/857687"], "description"=>"<p>We evaluated the contribution of each data set (namely: knock-down (‘<b>kd</b>’), time-series (‘<b>ts</b>’), knock-out (‘<b>ko</b>’), and all three combined (‘<b>all</b>’)) to performance of CLR, mixed-CLR, Inferelator 1.0, and CLR or mixed-CLR with Inferelator 1.0 (no filtration was used, ). Note, mixed-CLR is a generalization of CLR that takes advantage of time-series data, when time-series data is not used (i.e. for ‘kd’ and ‘ko’) the two are equivalent. For all tested methods ‘ko’ data contributes the most to performance (followed by ‘ts’ and ‘kd’ data respectively). The inclusion of a dynamical model allowed mixed-CLR and Inferelator 1.0 to take advantage of ‘ts’ data (compare to CLR above ‘ts’ and ‘all’ data partitions). Mixed-CLR and Inferelator 1.0 are complimentary, as evidenced by the improvement in performance when the two methods are combined. For ‘ts’, ‘ko’, and ‘all’ data partitions, mixed-CLR with Inferelator 1.0, the method we used to make predictions for DREAM3, gave optimal performance. Error bars for methods involving Inferelator 1.0 are drawn at one standard deviation (estimated from ten Inferelator 1.0 runs).</p>", "links"=>[], "tags"=>["biotechnology", "Computational biology", "biochemistry/transcription and translation", "biotechnology/bioengineering", "computational biology/genomics", "computational biology/transcriptional regulation"], "article_id"=>528139, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_as_a_function_of_data_set_used_/528139", "title"=>"Performance as a function of data set used.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:09:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/857562"], "description"=>"<p>For each regulatory interaction, , we define a confidence score , where indicates the step in our pipeline. We store these confidence scores in a corresponding matrix, (eq. 2), which we depict in the figure as a sorted list (from high to low confidence) of regulatory interactions. We schematically represent true positives (TPs) density (within any subset) as a gray scale, where black indicates high TP density. All possible pair-wise regulatory interactions are first scored using mixed-CLR, resulting in a matrix . We then filter out the least likely regulatory interactions based on the knock-out and knock-down steady-state observations, resulting in a matrix (the confidence score of each removed regulatory interaction was set to minus one, and thus sent to the back of the list). Lastly, we evaluate regulatory interactions in the TP enriched subset using Inferelator 1.0, by building an ODE model for each target gene. The kinetic weights from these ODE models were converted into confidence scores () and combined with to produce the final ranked list, (eq. 32). The regulatory interactions scored in , when ranked from high to low, represent our final ranking for each regulatory interaction.</p>", "links"=>[], "tags"=>["biotechnology", "Computational biology", "biochemistry/transcription and translation", "biotechnology/bioengineering", "computational biology/genomics", "computational biology/transcriptional regulation"], "article_id"=>528014, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Method_outline_/528014", "title"=>"Method outline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:08:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/857875"], "description"=>"<p>Here we evaluate the performance of mixed-CLR, filtration cutoff of , and Inferelator 1.0—the pipeline we applied to make DREAM3 predictions. Box plots for error distributions for each of the five predicted networks are shown in black in both panels, gray box plots show in-degree and out-degree distributions for <b>a.</b> and <b>b.</b> respectively. We estimated error in the following manner: Denote by the total number of possible regulatory interactions, and by the rank we gave to a regulatory interaction, , the relative rank (error) of is defined to be . <b>a</b>) Median relative rank (Error) increases as the networks' median in-degree increases (). <b>b</b>) Median relative rank (Error) is not correlated with median out-degree ().</p>", "links"=>[], "tags"=>["degrees"], "article_id"=>528336, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Error_as_a_function_of_gene_in_degrees_and_gene_out_degrees_/528336", "title"=>"Error as a function of gene in degrees and gene out degrees.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:10:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/858036"], "description"=>"<p>We computed static and dynamic Mutual Information (MI) values for every possible regulatory interaction for all five 100-gene networks. For both static and dynamic MI values, we computed z-scores for true regulatory interactions (true positives, TPs) and false regulatory interactions (true negatives, TNs). We present the static (<b>a.</b>) and dynamic (<b>b.</b>) z-scores densities (combined over the five 100-gene networks) for TPs (red) and TNs (green). Vertical lines represent median z-scores. We show that TPs are better separated from TNs by the dynamic MI z-scores, consistent with the improved performance of mixed- and dynamic-CLR.</p>", "links"=>[], "tags"=>["densities", "static"], "article_id"=>528497, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g007", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Probability_densities_of_static_and_dynamic_mutual_information_values_for_true_positive_and_true_negative_regulatory_interactions_/528497", "title"=>"Probability densities of static and dynamic mutual information values for true positive and true negative regulatory interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:11:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/857940"], "description"=>"<p>We computed static and dynamic Mutual Information (MI) values for every possible regulatory interaction. Vertical lines represent distribution means. We present the combined probability densities for the five -gene networks. We show that: 1) both dynamic and static MI densities are right skewed, consistent with the assumption that MI values of true positives would be higher than MI values of true negatives; 2) the standard deviations for static MI z-scores, , is larger than for dynamic MI z-scores, , possibly making it easier to recover TPs from the dynamic MI z-scores; and 3) most dynamic MI values are smaller than the mean of the static MI values; this shift confounds mixed-CLR. Note that both static- and dynamic-MI values were estimated from the same number of observations, using the same number of bins. Thus, dataset size or bin number differences do not explain the shift in distributions.</p>", "links"=>[], "tags"=>["static"], "article_id"=>528403, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distributions_of_static_vs_dynamic_mutual_information_values_/528403", "title"=>"Distributions of static vs. dynamic mutual information values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:10:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/857775"], "description"=>"<p>We present the relative merit of five methods, with and without knock-out filtration, to resolve causation (i.e. directionality of regulatory interactions). For each method we computed the fraction of correctly resolved true regulatory interactions (true positives, TPs) out of the total number of TPs the method had identified. We define a TP interaction, , as correctly resolved, if its score, (according to each method or method combination), was bigger than the confidence score of the reverse (false) regulatory interaction, . The original CLR method without filtration results in symmetric confidence scores, , and thus cannot resolve causation (fraction correct = ). In each bar plot we report the absolute number of correctly (incorrectly) resolved interactions. We show that, without filtration, Inferelator 1.0 has the most power at resolving causation ( correct), and that for all methods knock-out filtration helps resolve causation. For Inferelator 1.0 filtration helps recover more TPs. Error bars for methods involving Inferelator 1.0 are less than and are not shown.</p>", "links"=>[], "tags"=>["causation"], "article_id"=>528246, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Resolving_causation_of_regulatory_interactions_/528246", "title"=>"Resolving causation of regulatory interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 02:09:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/858148"], "description"=>"<p>In this table we present a more detailed view of performance for our method's poorest predicted network ( total regulatory interactions with up to regulators controlling each gene). The table inline method precision [%] at varying degrees of completeness (recall [%]).</p>", "links"=>[], "tags"=>["methods", "precision", "low-to-high"], "article_id"=>528606, "categories"=>["Biotechnology", "Biochemistry", "Computational Biology", "Infectious Diseases", "Biological Sciences"], "users"=>["Aviv Madar", "Alex Greenfield", "Eric Vanden-Eijnden", "Richard Bonneau"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0009803.t002", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_5_Yeast_3_Methods_precision_for_low_to_high_recall_values_/528606", "title"=>"Network 5 (Yeast-3): Methods precision for low-to-high recall values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 02:12:07"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"9"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"11"}
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  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
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  • {"unique-ip"=>"5", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"13", "full-text"=>"11", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"14", "full-text"=>"17", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"15", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"21", "full-text"=>"23", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"10", "full-text"=>"13", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"13", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"22", "full-text"=>"30", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"14", "full-text"=>"13", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"16", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"17", "full-text"=>"14", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"19", "full-text"=>"22", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"16", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"20", "full-text"=>"22", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"17", "full-text"=>"28", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"10", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}

Relative Metric

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