SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification
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{"title"=>"SlimPLS: A method for feature selection in gene expression-based disease classification", "type"=>"journal", "authors"=>[{"first_name"=>"Michael", "last_name"=>"Gutkin", "scopus_author_id"=>"7006301037"}, {"first_name"=>"Ron", "last_name"=>"Shamir", "scopus_author_id"=>"7005411902"}, {"first_name"=>"Gideon", "last_name"=>"Dror", "scopus_author_id"=>"6603356261"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"19649265", "scopus"=>"2-s2.0-68149171307", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0006416", "issn"=>"19326203", "sgr"=>"68149171307", "pui"=>"355036404"}, "id"=>"073646f1-de8b-35b1-aa62-cce9b3c8de76", "abstract"=>"A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique.", "link"=>"http://www.mendeley.com/research/slimpls-method-feature-selection-gene-expressionbased-disease-classification-2", "reader_count"=>38, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>11, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Student > Master"=>3, "Student > Bachelor"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>11, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Student > Master"=>3, "Student > Bachelor"=>1, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>3, "Mathematics"=>2, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>8, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>15, "Immunology and Microbiology"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>15}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Hong Kong"=>1, "Poland"=>1, "Slovenia"=>2, "Australia"=>1, "Spain"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/890290"], "description"=>"<p>Performance is measured by the average error rate of six different SlimPLS-based feature selectors using the KNN and SVM-radial classifiers, for different numbers of selected features.</p>", "links"=>[], "tags"=>["Computational biology", "computational biology/genomics", "computational biology/systems biology"], "article_id"=>560751, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_as_a_function_of_the_number_of_selected_features_/560751", "title"=>"Performance as a function of the number of selected features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:14:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/890740"], "description"=>"<p>In all variants the total number of features selected was set to 50 unless otherwise specified. n is a natural number and p is a real number 0</p>", "links"=>[], "tags"=>["slimpls", "variants"], "article_id"=>561196, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.t001", "stats"=>{"downloads"=>4, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_summary_of_the_SlimPLS_variants_and_their_properties_/561196", "title"=>"A summary of the SlimPLS variants and their properties.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 05:16:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/890452"], "description"=>"<p>−log(p-value) of the combined Wilcoxon rank-sum tests for the three groups using five different classifiers are shown. The concentric pentagons show the −log(p-value) scale. The results on separate classifiers are shown on the separate axes. This representation is intended to emphasize the relative performance of each FS on each classifier separately, and does not present relative performance across classifiers. See text for the description of the families.</p>", "links"=>[], "tags"=>["groups", "selectors", "calculated", "separately", "classifier-specific", "rank-sum"], "article_id"=>560909, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g005", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_three_groups_of_feature_selectors_calculated_separately_for_each_classifier_according_to_the_classifier_specific_rank_sum_p_value_/560909", "title"=>"Performance of three groups of feature selectors calculated separately for each classifier, according to the classifier-specific rank-sum p-value.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:15:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/890515"], "description"=>"<p>The comparison is done separately for each classifier: (A) SVM-linear (B) SVM-radial (C) Random Forest (D) KNN (E) Naïve Bayes. An edge X→Y indicates that X significantly outperformed Y. In (D) the second and the third layers from the top were originally one layer that was divided into two rows for display purposes only. Methods in upper layers performed better than methods in lower ones.</p>", "links"=>[], "tags"=>["maps", "comparing"], "article_id"=>560975, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g006", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dominance_maps_for_comparing_feature_selectors_/560975", "title"=>"Dominance maps for comparing feature selectors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:15:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/890765"], "description"=>"<p>Times are in seconds per iteration of the leave-one-out cross validation. The SVM-radial and KNN classifiers were used after the feature selection phase. These tests were done using a Linux platform with Intel Xeon 5160 CPU, 3.00 GHz clock, and 4 GB of RAM.</p>", "links"=>[], "tags"=>["Computational biology", "computational biology/genomics", "computational biology/systems biology"], "article_id"=>561232, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.t003", "stats"=>{"downloads"=>7, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_running_time_of_different_feature_selection_techniques_/561232", "title"=>"Average running time of different feature selection techniques.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 05:16:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/890195"], "description"=>"<p>The prediction stage of the PLS algorithm.</p>", "links"=>[], "tags"=>["pls"], "article_id"=>560657, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g002", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_prediction_stage_of_the_PLS_algorithm_/560657", "title"=>"The prediction stage of the PLS algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:13:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/890803"], "description"=>"<p>Datasets 12–19 were used in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006416#pone.0006416-Song1\" target=\"_blank\">[40]</a>. For each dataset, N is the number of samples, A and B are the number of cases and controls, respectively, and P is the number of probes (features) measured. HD: Huntington's Disease.</p>", "links"=>[], "tags"=>["datasets"], "article_id"=>561266, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.t002", "stats"=>{"downloads"=>8, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Datasets_used_in_this_study_/561266", "title"=>"The Datasets used in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 05:17:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/890038"], "description"=>"<p>The learning stage of the PLS algorithm.</p>", "links"=>[], "tags"=>["pls"], "article_id"=>560499, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_learning_stage_of_the_PLS_algorithm_/560499", "title"=>"The learning stage of the PLS algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:12:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/890685"], "description"=>"<p>Additional singletons not shown in the picture: KNN - 5×10<sup>−3</sup>-HC-TOP and 1-HIGH-TOP; SVM-linear–1-HIGH-TOP and COR; RF - 5×10<sup>−3</sup>-HC-TOP; NB - 5×10<sup>−3</sup>-HC-TCOMP.</p>", "links"=>[], "tags"=>["dominance", "comparing", "selectors"], "article_id"=>561143, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g007", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_dominance_map_comparing_the_performance_of_four_leading_feature_selectors_using_all_classifiers_/561143", "title"=>"A dominance map comparing the performance of four leading feature selectors using all classifiers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:16:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/890370"], "description"=>"<p>−log(p values) of the combined Wilcoxon rank-sum tests for three groups of methods using five different classifiers are shown. See text for the group definitions.</p>", "links"=>[], "tags"=>["classifiers", "groups"], "article_id"=>560827, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Michael Gutkin", "Ron Shamir", "Gideon Dror"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006416.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_different_classifiers_using_three_groups_of_feature_selectors_/560827", "title"=>"Performance of different classifiers using three groups of feature selectors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 05:14:41"}

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  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"7"}
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  • {"unique-ip"=>"9", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2014", "month"=>"9"}
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  • {"unique-ip"=>"5", "full-text"=>"2", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"1"}
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  • {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
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  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"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"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"2", "full-text"=>"4", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"3", "full-text"=>"0", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"8", "full-text"=>"8", "pdf"=>"4", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"5", "full-text"=>"5", "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"=>"20", "full-text"=>"38", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"11", "full-text"=>"26", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"15", "full-text"=>"38", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2017", "month"=>"9"}
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  • {"unique-ip"=>"12", "full-text"=>"20", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"10", "full-text"=>"18", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"8", "full-text"=>"18", "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"=>"7", "full-text"=>"6", "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"=>"7", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"13", "full-text"=>"13", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"16", "full-text"=>"16", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"11", "full-text"=>"16", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"14", "full-text"=>"13", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"18", "full-text"=>"20", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"7", "full-text"=>"3", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
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Relative Metric

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