Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data
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{"title"=>"Ontology-based meta-analysis of global collections of high-throughput public data", "type"=>"journal", "authors"=>[{"first_name"=>"Ilya", "last_name"=>"Kupershmidt", "scopus_author_id"=>"8906048100"}, {"first_name"=>"Qiaojuan Jane", "last_name"=>"Su", "scopus_author_id"=>"57197944292"}, {"first_name"=>"Anoop", "last_name"=>"Grewal", "scopus_author_id"=>"16136771400"}, {"first_name"=>"Suman", "last_name"=>"Sundaresh", "scopus_author_id"=>"54922817600"}, {"first_name"=>"Inbal", "last_name"=>"Halperin", "scopus_author_id"=>"37037307400"}, {"first_name"=>"James", "last_name"=>"Flynn", "scopus_author_id"=>"57197318046"}, {"first_name"=>"Mamatha", "last_name"=>"Shekar", "scopus_author_id"=>"37038461100"}, {"first_name"=>"Helen", "last_name"=>"Wang", "scopus_author_id"=>"57189504133"}, {"first_name"=>"Jenny", "last_name"=>"Park", "scopus_author_id"=>"37038185500"}, {"first_name"=>"Wenwu", "last_name"=>"Cui", "scopus_author_id"=>"7202733832"}, {"first_name"=>"Gregory D.", "last_name"=>"Wall", "scopus_author_id"=>"37038833500"}, {"first_name"=>"Robert", "last_name"=>"Wisotzkey", "scopus_author_id"=>"6506110993"}, {"first_name"=>"Satnam", "last_name"=>"Alag", "scopus_author_id"=>"6506544653"}, {"first_name"=>"Saeid", "last_name"=>"Akhtari", "scopus_author_id"=>"37036903300"}, {"first_name"=>"Mostafa", "last_name"=>"Ronaghi", "scopus_author_id"=>"7004353825"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-77958590561", "sgr"=>"77958590561", "pui"=>"359839295", "isbn"=>"1932-6203 (Electronic) 1932-6203 (Linking)", "pmid"=>"20927376", "doi"=>"10.1371/journal.pone.0013066"}, "id"=>"a60c6d78-76c6-318c-b26c-3d28b85b5395", "abstract"=>"BACKGROUND: The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.\\n\\nMETHODOLOGY/RESULTS: We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.\\n\\nCONCLUSIONS: Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.", "link"=>"http://www.mendeley.com/research/ontologybased-metaanalysis-global-collections-highthroughput-public-data", "reader_count"=>145, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>11, "Librarian"=>1, "Researcher"=>55, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>3, "Other"=>13, "Student > Master"=>8, "Student > Bachelor"=>6, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>11, "Librarian"=>1, "Researcher"=>55, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>34, "Student > Postgraduate"=>3, "Other"=>13, "Student > Master"=>8, "Student > Bachelor"=>6, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Agricultural and Biological Sciences"=>70, "Arts and Humanities"=>1, "Chemistry"=>4, "Computer Science"=>22, "Engineering"=>4, "Biochemistry, Genetics and Molecular Biology"=>16, "Mathematics"=>2, "Medicine and Dentistry"=>10, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>3, "Psychology"=>1, "Social Sciences"=>5}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Social Sciences"=>{"Social Sciences"=>5}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>6}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>4}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>70}, "Computer Science"=>{"Computer Science"=>22}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>16}}, "reader_count_by_country"=>{"United States"=>9, "Uruguay"=>1, "Japan"=>2, "Egypt"=>1, "United Kingdom"=>3, "Portugal"=>1, "India"=>1, "Russia"=>1, "Canada"=>3, "China"=>1, "Ireland"=>1, "Finland"=>1, "Denmark"=>1, "Italy"=>1, "Slovenia"=>1}, "group_count"=>13}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/828048"], "description"=>"<p>Brown fat and muscle normal tissue signatures queried against all disease-related signatures in different studies and organisms. Top diseases with negatively correlated genes to brown fat and muscle are shown (for additional results see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013066#pone.0013066.s003\" target=\"_blank\">Table S3</a>, Supporting Information section).</p>", "links"=>[], "tags"=>["signatures"], "article_id"=>498402, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t004", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_brown_fat_and_muscle_tissue_signatures_with_diseases_/498402", "title"=>"Correlation between brown fat and muscle tissue signatures with diseases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:20:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/827260"], "description"=>"<p>The steps for turning public datasets into processed gene signatures include: raw data collection, sample annotation curation, data quality control, automated analysis, and manual tagging of resulting signatures with disease, tissue, compound ontology, and gene perturbation terms (tags). Curation of sample annotation includes a systematic analysis of all sample attributes that should be processed for differential expression. The data processing step converts original raw data into processed results – gene expression signatures representative of a given biological condition. The final tagging step ensures that key biological conditions associated with each signature are captured with standardized vocabulary terms, enabling downstream meta-analysis.</p>", "links"=>[], "tags"=>["pipeline"], "article_id"=>497606, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.g001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Public_data_processing_and_analysis_pipeline_diagram_/497606", "title"=>"Public data processing and analysis pipeline diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-29 02:06:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/827616"], "description"=>"<p>First, pairwise gene signature correlation scores (using rank-based enrichment statistics) are computed, followed by meta-analysis of individual score-tag pairs to compute overall tag scores. This two step process results in computation of direct correlations between user's defined signature and diverse biological conditions representing normal tissues and cell types, diseases, and compounds. Furthermore, overall positive or negative correlation between a signature and a concept is computed based on individual pairwise signature correlation scores. A positive correlation implies a similar up- and down-regulation of genes in each signature or signature-tag pair, while a negative correlation implies the opposite trend.</p>", "links"=>[], "tags"=>["query", "signatures"], "article_id"=>497963, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_signature_query_against_all_other_signatures_within_the_system_/497963", "title"=>"Gene signature query against all other signatures within the system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-29 02:12:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/827899"], "description"=>"<p>Total concepts count represents the number of specific ontology terms that are assigned as tags to signatures, or which represent “parent” ontology concepts. For example, when a gene signature is tagged with “heart ventricle”, it is automatically considered tagged with the parent term “heart” and both are considered in the counts shown. “Total studies” refers to the number of studies that contain gene signatures that contribute to a given concept based on their associated tags. “Total Signatures” and “Total Samples” refer to the number of gene signatures and individual samples contributing to a given concept type (e.g. disease).</p>", "links"=>[], "tags"=>["Computational biology", "computational biology/transcriptional regulation", "genetics and genomics/bioinformatics", "genetics and genomics/functional genomics", "genetics and genomics/gene expression", "diabetes and endocrinology/obesity"], "article_id"=>498242, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_all_data_associated_with_normal_tissues_diseases_drug_treatments_and_genetic_perturbations_/498242", "title"=>"Summary of all data associated with normal tissues, diseases, drug treatments, and genetic perturbations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:17:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/827999"], "description"=>"<p>Query results for gene expression signature differentiating brown and white preadipocytes across normal tissue signatures. Positive correlation scores for the top four tissues whose expression signatures correlate with brown vs. white preadipocytes signature (for additional results see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013066#pone.0013066.s005\" target=\"_blank\">Table S5</a>, Supporting Information section).</p>", "links"=>[], "tags"=>["preadipocytes", "tissues"], "article_id"=>498350, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brown_versus_white_preadipocytes_signature_correlation_with_normal_tissues_and_cell_types_/498350", "title"=>"Brown versus white preadipocytes signature correlation with normal tissues and cell types.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:19:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/828156"], "description"=>"<p>Top five query results for gene expression signature comparing brown to white preadipocytes across all signatures tagged with “Genetic Perturbation” category (for additional results see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013066#pone.0013066.s008\" target=\"_blank\">Table S8</a>, Supporting Information section).</p>", "links"=>[], "tags"=>["preadipocytes"], "article_id"=>498509, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t006", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brown_versus_white_preadipocytes_signature_correlation_with_genetic_perturbations_/498509", "title"=>"Brown versus white preadipocytes signature correlation with genetic perturbations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:21:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/412025", "https://ndownloader.figshare.com/files/412037", "https://ndownloader.figshare.com/files/412050", "https://ndownloader.figshare.com/files/412062", "https://ndownloader.figshare.com/files/412071", "https://ndownloader.figshare.com/files/412079", "https://ndownloader.figshare.com/files/412089", "https://ndownloader.figshare.com/files/412097"], "description"=>"<div><h3>Background</h3><p>The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today.</p><h3>Methodology/Results</h3><p>We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets.</p><h3>Conclusions</h3><p>Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.</p></div>", "links"=>[], "tags"=>["ontology-based", "meta-analysis", "collections", "high-throughput"], "article_id"=>141389, "categories"=>["Biological Sciences", "Genetics"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0013066.s001", "https://dx.doi.org/10.1371/journal.pone.0013066.s002", "https://dx.doi.org/10.1371/journal.pone.0013066.s003", "https://dx.doi.org/10.1371/journal.pone.0013066.s004", "https://dx.doi.org/10.1371/journal.pone.0013066.s005", "https://dx.doi.org/10.1371/journal.pone.0013066.s006", "https://dx.doi.org/10.1371/journal.pone.0013066.s007", "https://dx.doi.org/10.1371/journal.pone.0013066.s008"], "stats"=>{"downloads"=>44, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Ontology_Based_Meta_Analysis_of_Global_Collections_of_High_Throughput_Public_Data/141389", "title"=>"Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-09-29 00:23:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/827362"], "description"=>"<p>The algorithm represented by this schematic computes an enrichment score and p-value between two ranked gene signatures. Dark red and blue colored boxes indicate genes present in both signatures; light red and blue colored boxes represent genes present in only one of the signatures. Dark lines connecting genes in each signature represent connections between genes with the same direction of regulation in both signatures. Light lines connect genes with opposite direction in two signatures.</p>", "links"=>[], "tags"=>["pairwise"], "article_id"=>497715, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.g002", "stats"=>{"downloads"=>2, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computing_pairwise_signature_correlation_scores_/497715", "title"=>"Computing pairwise signature correlation scores.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-29 02:08:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/827754"], "description"=>"<p>Diagram representing analyses of two different brown fat related signatures: (a) Brown fat tissue signature (relative to all other mouse tissues). (b) Signature of brown preadipocytes vs. white preadipocytes. After computing pairwise scores between query and all target signatures the meta-analysis of pairwise scores and their associated tags (associated disease, tissue, and compound terms) is performed. The final result produces a ranked set of tissues, diseases, and compounds with the most significant association to query signature.</p>", "links"=>[], "tags"=>["Computational biology", "computational biology/transcriptional regulation", "genetics and genomics/bioinformatics", "genetics and genomics/functional genomics", "genetics and genomics/gene expression", "diabetes and endocrinology/obesity"], "article_id"=>498094, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brown_fat_meta_analysis_/498094", "title"=>"Brown fat meta-analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-29 02:14:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/828106"], "description"=>"<p>Top five query results for gene expression signature comparing mature brown adipocytes to differentiating brown preadipocytes across all signatures tagged with “Compounds” category (for additional results see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013066#pone.0013066.s006\" target=\"_blank\">Table S6</a>, Supporting Information section).</p>", "links"=>[], "tags"=>["mature", "adipocytes"], "article_id"=>498454, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t005", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brown_mature_adipocytes_signature_correlation_with_compounds_/498454", "title"=>"Brown mature adipocytes signature correlation with compounds.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:20:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/827946"], "description"=>"<p>The gene expression signature from brown fat tissue was queried against all studies corresponding to normal normal tissues from different microarray platforms and organisms. The results for top five tissues with the biggest positive correlation to brown fat signature are shown. Additional results are shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013066#pone.0013066.s002\" target=\"_blank\">Tabs S2</a> of the Supporting Information section.</p>", "links"=>[], "tags"=>["query"], "article_id"=>498297, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brown_fat_tissue_signature_query_results_/498297", "title"=>"Brown fat tissue signature query results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-29 02:18:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/827519"], "description"=>"<p>The directional subsets are formed for both b1 and b2, and subset-subset enrichment scores are Computed for b1<sup>+</sup>b2<sup>+</sup>, b1<sup>+</sup>b2<sup>−</sup>, b1<sup>−</sup>b2<sup>+</sup>, and b1<sup>−</sup>b2<sup>−</sup>. Pairwise correlation scores for the directional subsets are positive where subsets are of the same direction and negative sign otherwise. The correlation scores of the subsets are summed up to give the final score for full set b1 versus full set b2.</p>", "links"=>[], "tags"=>["directionality", "scores"], "article_id"=>497871, "categories"=>["Computational Biology", "Genetics", "Biological Sciences"], "users"=>["Ilya Kupershmidt", "Qiaojuan Jane Su", "Anoop Grewal", "Suman Sundaresh", "Inbal Halperin", "James Flynn", "Mamatha Shekar", "Helen Wang", "Jenny Park", "Wenwu Cui", "Gregory D. Wall", "Robert Wisotzkey", "Satnam Alag", "Saeid Akhtari", "Mostafa Ronaghi"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0013066.g003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computing_directionality_and_final_correlation_scores_between_two_signatures_/497871", "title"=>"Computing directionality and final correlation scores between two signatures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-29 02:11:11"}

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  • {"unique-ip"=>"14", "full-text"=>"12", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"35", "full-text"=>"42", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"15", "supp-data"=>"8", "cited-by"=>"1", "year"=>"2019", "month"=>"9"}

Relative Metric

{"start_date"=>"2010-01-01T00:00:00Z", "end_date"=>"2010-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[288, 576, 733, 867, 984, 1087, 1182, 1267, 1346, 1424, 1501, 1577, 1646, 1711, 1778, 1841, 1908, 1970, 2034, 2102, 2162, 2227, 2296, 2359, 2422, 2482, 2550, 2610, 2679, 2747, 2820, 2887, 2955, 3009, 3067, 3130, 3200, 3257, 3322, 3379, 3443, 3507, 3571, 3632, 3683, 3753, 3822, 3877]}, {"subject_area"=>"/Biology and life sciences/Anatomy", "average_usage"=>[275, 532, 682, 800, 904, 1008, 1103, 1194, 1265, 1340, 1412, 1479, 1546, 1610, 1672, 1736, 1793, 1861, 1922, 1978, 2032, 2094, 2155, 2220, 2279, 2333, 2395, 2481, 2543, 2601, 2669, 2722, 2767, 2826, 2882, 2942, 3006, 3054, 3110, 3176, 3253, 3313, 3367, 3422, 3472, 3539, 3593, 3655, 3703]}, {"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[280, 562, 725, 863, 974, 1083, 1177, 1268, 1347, 1421, 1489, 1570, 1638, 1706, 1763, 1823, 1890, 1951, 2016, 2076, 2134, 2192, 2257, 2319, 2378, 2438, 2501, 2572, 2634, 2700, 2759, 2825, 2887, 2936, 3007, 3070, 3121, 3184, 3237, 3304, 3363, 3425, 3484, 3531, 3612, 3663, 3718, 3771]}, {"subject_area"=>"/Biology and life sciences/Developmental biology", "average_usage"=>[265, 560, 722, 863, 978, 1083, 1172, 1261, 1345, 1428, 1488, 1559, 1630, 1701, 1764, 1840, 1899, 1965, 2028, 2088, 2150, 2211, 2270, 2348, 2410, 2461, 2531, 2598, 2645, 2719, 2793, 2870, 2923, 2974, 3020, 3080, 3142, 3198, 3259, 3351, 3418, 3471, 3527, 3581, 3637, 3706, 3761, 3817, 3872]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[348, 644, 769, 882, 976, 1040, 1128, 1209, 1272, 1322, 1411, 1506, 1576, 1641, 1700, 1753, 1794, 1857, 1922, 1991, 2058, 2147, 2203, 2266, 2337, 2418, 2509, 2575, 2639, 2714, 2782, 2838, 2900, 2958, 3009, 3137, 3215, 3339, 3435, 3548, 3641, 3737, 3806, 3868, 3930, 3991, 4050, 4101, 4151]}, {"subject_area"=>"/Medicine and health sciences/Anatomy and physiology", "average_usage"=>[270, 536, 692, 812, 932, 1037, 1128, 1210, 1293, 1367, 1439, 1514, 1580, 1646, 1711, 1766, 1834, 1902, 1969, 2036, 2106, 2165, 2221, 2285, 2337, 2403, 2459, 2517, 2586, 2644, 2716, 2782, 2842, 2895, 2958, 3017]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[286, 543, 687, 806, 915, 1004, 1092, 1169, 1234, 1309, 1372, 1435, 1502, 1563, 1627, 1685, 1747, 1796, 1859, 1921, 1977, 2032, 2093, 2157, 2210, 2278, 2333, 2406, 2463, 2535, 2590, 2642, 2692, 2753, 2813, 2872, 2941, 2989, 3040, 3080, 3151, 3218, 3269, 3323, 3375, 3437, 3500, 3550, 3604]}]}
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Net::HTTPTooManyRequests

Source
Scopus
Time
2019-08-27 20:09:05 UTC
Target URL
https://api.elsevier.com/content/search/index:SCOPUS?query=DOI(10.1371%2Fjournal.pone.0013066)
Trace

/app/models/concerns/networkable.rb:21:in `get_result'
/app/models/source.rb:165:in `get_data'
/app/models/retrieval_status.rb:47:in `perform_get_data'
/app/jobs/source_job.rb:52:in `block (2 levels) in perform'
/app/jobs/source_job.rb:51:in `block in perform'
/app/jobs/source_job.rb:35:in `each'
/app/jobs/source_job.rb:35:in `perform'