Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data
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{"title"=>"Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data", "type"=>"journal", "authors"=>[{"first_name"=>"Kristen", "last_name"=>"Fortney", "scopus_author_id"=>"36099931500"}, {"first_name"=>"Joshua", "last_name"=>"Griesman", "scopus_author_id"=>"57193910599"}, {"first_name"=>"Max", "last_name"=>"Kotlyar", "scopus_author_id"=>"12546091800"}, {"first_name"=>"Chiara", "last_name"=>"Pastrello", "scopus_author_id"=>"10540970300"}, {"first_name"=>"Marc", "last_name"=>"Angeli", "scopus_author_id"=>"56581698700"}, {"first_name"=>"Ming", "last_name"=>"Sound-Tsao", "scopus_author_id"=>"7102152241"}, {"first_name"=>"Igor", "last_name"=>"Jurisica", "scopus_author_id"=>"7003966366"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "scopus"=>"2-s2.0-84926385334", "pui"=>"603513928", "doi"=>"10.1371/journal.pcbi.1004068", "isbn"=>"10.1371/journal.pcbi.1004068", "sgr"=>"84926385334", "pmid"=>"25786242"}, "id"=>"0a7ebaf5-41df-3fd3-9e53-1c6ef5a6daac", "abstract"=>"Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.", "link"=>"http://www.mendeley.com/research/prioritizing-therapeutics-lung-cancer-integrative-metaanalysis-cancer-gene-signatures-chemogenomic-d", "reader_count"=>57, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>2, "Student > Master"=>5, "Student > Bachelor"=>8, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>14, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>2, "Student > Master"=>5, "Student > Bachelor"=>8, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>9, "Agricultural and Biological Sciences"=>22, "Medicine and Dentistry"=>7, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>4, "Social Sciences"=>1, "Psychology"=>1, "Business, Management and Accounting"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1958282"], "description"=>"<p>We tested whether the drugs that we identified as significantly reversing the gene changes seen with lung cancer were better at inhibiting growth using NCI-60 GI50 data in 9 lung cancer cell lines. In every cell line, significant drugs are better than other Connectivity Map drugs at inhibiting growth (Wilcox test P < 0.01). On the y axis, growth inhibition is presented as-log10 GI50, so that e.g. a value of 6 corresponds to a pGI50 of 10<sup>-6</sup> M.</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341069, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g003", "stats"=>{"downloads"=>0, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Drug_candidates_inhibit_growth_in_lung_cancer_cell_lines_more_than_other_Connectivity_Map_drugs_/1341069", "title"=>"Drug candidates inhibit growth in lung cancer cell lines more than other Connectivity Map drugs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958281"], "description"=>"<p>Shown are boxplots of the number of conserved drug candidates when any two lists of top 50 drug candidates are intersected. Green: 21 gene signatures were split into two disjoint sets of 10 and 11 signatures, CMapBatch was run on both sets, and top drugs from each set were compared; this experiment was repeated 100 times. Blue: 21 gene signatures were used to retrieve 21 lists of drugs with the CMap online tool; top drugs from all pairs of signatures were compared. Grey: 10 gene signatures of the same lung cancer type (adenocarcinoma) were used to retrieve 10 lists of drugs with the CMap online tool; top drugs from all pairs of signatures were compared. CMapBatch results showed a significantly higher median overlap (Wilcox test P << 0.01).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341068, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g002", "stats"=>{"downloads"=>2, "page_views"=>30, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CMapBatch_produces_more_stable_lists_of_significant_drugs_than_individual_gene_signatures_/1341068", "title"=>"CMapBatch produces more stable lists of significant drugs than individual gene signatures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958295"], "description"=>"<p>Common protein targets of candidate drugs.</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341082, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.t002", "stats"=>{"downloads"=>3, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Common_protein_targets_of_candidate_drugs_/1341082", "title"=>"Common protein targets of candidate drugs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958289"], "description"=>"<p>We used CMap data to calculate the number of genes that were significantly differentially regulated (P < 0.05) for each of 1,309 drugs. Drugs that we identified as reversing the gene changes seen with lung cancer affected significantly more genes than other drugs (median of 8.5 vs. 3 genes; Wilcox test P << 0.01).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341076, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g006", "stats"=>{"downloads"=>6, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_drugs_affect_more_genes_than_other_Connectivity_Map_drugs_/1341076", "title"=>"Significant drugs affect more genes than other Connectivity Map drugs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958283"], "description"=>"<p>Twenty-three of the significant drugs inhibit growth in a majority of lung cancer cell lines (top). A further 11 significant drugs not tested in NCI-60 are highly structurally similar (Tanimoto similarity > = 0.8) to one or more of the sixteen (bottom).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341070, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g004", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prioritizing_drug_candidates_with_GI50_values_and_chemical_structures_/1341070", "title"=>"Prioritizing drug candidates with GI50 values and chemical structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958294"], "description"=>"<p>Twenty-one lung cancer gene signatures (tumour vs. normal comparisons).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341081, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.t001", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Twenty_one_lung_cancer_gene_signatures_tumour_vs_normal_comparisons_/1341081", "title"=>"Twenty-one lung cancer gene signatures (tumour vs. normal comparisons).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958263"], "description"=>"<p>Given a set of disease signatures, CMapBatch calculates mean connectivity scores for 1,309 drugs and converts them to ranks. Next, CMapBatch applies the Rank Product method to identify drugs that are consistently highly ranked across signatures. On a set of 21 transcriptional signatures of lung cancer, we identified 247 drugs that significantly reverse these pathological gene expression changes (at FDR < 1%).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341066, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g001", "stats"=>{"downloads"=>1, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CMapBatch_meta_analysis_pipeline_/1341066", "title"=>"CMapBatch meta-analysis pipeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958293"], "description"=>"<p>Results of the MTT assay in A549, HCC4006, H1437, and H4006 cell lines. Bar height indicates the mean and error bars the standard deviation of 3 biological replicates. Y-axis shows percent viability relative to untreated cells. In each cancer cell line, pimozide shows a significant cytotoxic effect. Asterisk indicates P < 0.05 (t-test).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341080, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g008", "stats"=>{"downloads"=>1, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pimozide_reduces_viability_in_four_lung_cancer_cell_lines_/1341080", "title"=>"Pimozide reduces viability in four lung cancer cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958291"], "description"=>"<p>We ran CMapBatch on 10 adenocarcinoma signatures only, and on 6 squamous cell carcinoma signatures only. 79 drugs were common to the lists of top 100 drugs for both cancer subtypes.</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341078, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g007", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Drugs_treat_multiple_subtypes_of_lung_cancer_/1341078", "title"=>"Drugs treat multiple subtypes of lung cancer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958288"], "description"=>"<p><b>A</b>. In the drug-target network for drug candidates, two drugs are connected by an edge if they have the same protein target. Shown in colour are the drugs that slow growth in 5 or more lung cancer cell lines (blue), their immediate neighbours (purple), and the drugs that are structurally similar to them (green). Green edges indicate drug pairs that, in addition to sharing a protein target, were also found to be highly structurally similar (see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004068#pcbi.1004068.g004\" target=\"_blank\">Fig. 4</a>). <b>B</b>. 83 significant drugs are represented in the drug-target network, and the largest connected component contains 72 drugs. 10,000 random draws of 83 drugs from the drug-target network resulted in smaller connected components (median size 42 drugs; P << 0.01).</p>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341075, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004068.g005", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Significant_drugs_share_many_protein_targets_/1341075", "title"=>"Significant drugs share many protein targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-18 04:15:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/1958303", "https://ndownloader.figshare.com/files/1958304", "https://ndownloader.figshare.com/files/1958305", "https://ndownloader.figshare.com/files/1958306", "https://ndownloader.figshare.com/files/1958307"], "description"=>"<div><p>Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits <i>in silico</i>, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain.</p></div>", "links"=>[], "tags"=>["2G", "disease signatures", "Chemogenomic Data Repurposing", "Different gene signatures", "drug hits", "pla", "transcriptional knowledge", "CALM", "drug predictions", "lung cancer transcriptomes", "Cancer Gene Signatures", "nci", "lung cancer cell lines"], "article_id"=>1341089, "categories"=>["Biological Sciences"], "users"=>["Kristen Fortney", "Joshua Griesman", "Max Kotlyar", "Chiara Pastrello", "Marc Angeli", "Ming Sound-Tsao", "Igor Jurisica"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004068.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004068.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004068.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004068.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004068.s005"], "stats"=>{"downloads"=>17, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prioritizing_Therapeutics_for_Lung_Cancer_An_Integrative_Meta_analysis_of_Cancer_Gene_Signatures_and_Chemogenomic_Data_/1341089", "title"=>"Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-18 04:15:22"}

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  • {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"12", "full-text"=>"17", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"11", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"7", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"14", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"7", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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