Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression
Events
Loading … Spinner

Mendeley | Further Information

{"title"=>"Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression", "type"=>"journal", "authors"=>[{"first_name"=>"Joanna F.", "last_name"=>"Dipnall", "scopus_author_id"=>"56282124800"}, {"first_name"=>"Julie A.", "last_name"=>"Pasco", "scopus_author_id"=>"7003647960"}, {"first_name"=>"Michael", "last_name"=>"Berk", "scopus_author_id"=>"7102183860"}, {"first_name"=>"Lana J.", "last_name"=>"Williams", "scopus_author_id"=>"8922705500"}, {"first_name"=>"Seetal", "last_name"=>"Dodd", "scopus_author_id"=>"7102997494"}, {"first_name"=>"Felice N.", "last_name"=>"Jacka", "scopus_author_id"=>"6603183351"}, {"first_name"=>"Denny", "last_name"=>"Meyer", "scopus_author_id"=>"7403571635"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84959423338", "doi"=>"10.1371/journal.pone.0148195", "issn"=>"19326203", "pui"=>"608547234", "isbn"=>"1932-6203", "pmid"=>"26848571", "scopus"=>"2-s2.0-84959423338", "arxiv"=>"ISSN: 1932-6203 (Online)"}, "id"=>"6a01335e-4cb9-3b47-bc9a-c60a9df32b45", "abstract"=>"BACKGROUND Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. METHODS The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. RESULTS After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). CONCLUSION The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.", "link"=>"http://www.mendeley.com/research/fusing-data-mining-machine-learning-traditional-statistics-detect-biomarkers-associated-depression", "reader_count"=>70, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>13, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>13, "Student > Bachelor"=>8, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>13, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Other"=>3, "Student > Master"=>13, "Student > Bachelor"=>8, "Lecturer"=>5, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Agricultural and Biological Sciences"=>7, "Arts and Humanities"=>1, "Business, Management and Accounting"=>1, "Computer Science"=>9, "Decision Sciences"=>1, "Energy"=>1, "Engineering"=>3, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>9, "Neuroscience"=>7, "Psychology"=>10, "Social Sciences"=>4, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Social Sciences"=>{"Social Sciences"=>4}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>10}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>8}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>3}, "Neuroscience"=>{"Neuroscience"=>7}, "Energy"=>{"Energy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>9}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}}, "reader_count_by_country"=>{"United States"=>2, "Ireland"=>1, "United Kingdom"=>1, "Switzerland"=>1, "Spain"=>1}, "group_count"=>8}

Scopus | Further Information

{"@_fa"=>"true", "link"=>[{"@_fa"=>"true", "@ref"=>"self", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84959423338"}, {"@_fa"=>"true", "@ref"=>"author-affiliation", "@href"=>"https://api.elsevier.com/content/abstract/scopus_id/84959423338?field=author,affiliation"}, {"@_fa"=>"true", "@ref"=>"scopus", "@href"=>"https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959423338&origin=inward"}, {"@_fa"=>"true", "@ref"=>"scopus-citedby", "@href"=>"https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84959423338&origin=inward"}], "prism:url"=>"https://api.elsevier.com/content/abstract/scopus_id/84959423338", "dc:identifier"=>"SCOPUS_ID:84959423338", "eid"=>"2-s2.0-84959423338", "dc:title"=>"Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression", "dc:creator"=>"Dipnall J.F.", "prism:publicationName"=>"PLoS ONE", "prism:eIssn"=>"19326203", "prism:volume"=>"11", "prism:issueIdentifier"=>"2", "prism:pageRange"=>nil, "prism:coverDate"=>"2016-02-01", "prism:coverDisplayDate"=>"1 February 2016", "prism:doi"=>"10.1371/journal.pone.0148195", "citedby-count"=>"31", "affiliation"=>[{"@_fa"=>"true", "affilname"=>"Swinburne University of Technology", "affiliation-city"=>"Melbourne", "affiliation-country"=>"Australia"}, {"@_fa"=>"true", "affilname"=>"Deakin University", "affiliation-city"=>"Geelong", "affiliation-country"=>"Australia"}], "pubmed-id"=>"26848571", "prism:aggregationType"=>"Journal", "subtype"=>"ar", "subtypeDescription"=>"Article", "article-number"=>"e0148195", "source-id"=>"10600153309", "openaccess"=>"1", "openaccessFlag"=>true}

Facebook

  • {"url"=>"http%3A%2F%2Fjournals.plos.org%2Fplosone%2Farticle%3Fid%3D10.1371%252Fjournal.pone.0148195", "share_count"=>1, "like_count"=>1, "comment_count"=>0, "click_count"=>0, "total_count"=>2}

Twitter

Counter

  • {"month"=>"2", "year"=>"2016", "pdf_views"=>"22", "xml_views"=>"2", "html_views"=>"453"}
  • {"month"=>"3", "year"=>"2016", "pdf_views"=>"50", "xml_views"=>"0", "html_views"=>"227"}
  • {"month"=>"4", "year"=>"2016", "pdf_views"=>"35", "xml_views"=>"1", "html_views"=>"159"}
  • {"month"=>"5", "year"=>"2016", "pdf_views"=>"36", "xml_views"=>"0", "html_views"=>"128"}
  • {"month"=>"6", "year"=>"2016", "pdf_views"=>"39", "xml_views"=>"0", "html_views"=>"127"}
  • {"month"=>"7", "year"=>"2016", "pdf_views"=>"34", "xml_views"=>"0", "html_views"=>"92"}
  • {"month"=>"8", "year"=>"2016", "pdf_views"=>"43", "xml_views"=>"0", "html_views"=>"121"}
  • {"month"=>"9", "year"=>"2016", "pdf_views"=>"28", "xml_views"=>"0", "html_views"=>"99"}
  • {"month"=>"10", "year"=>"2016", "pdf_views"=>"25", "xml_views"=>"0", "html_views"=>"104"}
  • {"month"=>"11", "year"=>"2016", "pdf_views"=>"23", "xml_views"=>"0", "html_views"=>"128"}
  • {"month"=>"12", "year"=>"2016", "pdf_views"=>"35", "xml_views"=>"0", "html_views"=>"122"}
  • {"month"=>"1", "year"=>"2017", "pdf_views"=>"32", "xml_views"=>"2", "html_views"=>"109"}
  • {"month"=>"2", "year"=>"2017", "pdf_views"=>"45", "xml_views"=>"1", "html_views"=>"163"}
  • {"month"=>"3", "year"=>"2017", "pdf_views"=>"31", "xml_views"=>"0", "html_views"=>"132"}
  • {"month"=>"4", "year"=>"2017", "pdf_views"=>"40", "xml_views"=>"0", "html_views"=>"137"}
  • {"month"=>"5", "year"=>"2017", "pdf_views"=>"46", "xml_views"=>"1", "html_views"=>"133"}
  • {"month"=>"6", "year"=>"2017", "pdf_views"=>"26", "xml_views"=>"0", "html_views"=>"118"}
  • {"month"=>"7", "year"=>"2017", "pdf_views"=>"34", "xml_views"=>"2", "html_views"=>"107"}
  • {"month"=>"8", "year"=>"2017", "pdf_views"=>"19", "xml_views"=>"1", "html_views"=>"106"}
  • {"month"=>"9", "year"=>"2017", "pdf_views"=>"20", "xml_views"=>"1", "html_views"=>"102"}
  • {"month"=>"10", "year"=>"2017", "pdf_views"=>"26", "xml_views"=>"0", "html_views"=>"150"}
  • {"month"=>"11", "year"=>"2017", "pdf_views"=>"19", "xml_views"=>"0", "html_views"=>"216"}
  • {"month"=>"12", "year"=>"2017", "pdf_views"=>"28", "xml_views"=>"1", "html_views"=>"367"}
  • {"month"=>"1", "year"=>"2018", "pdf_views"=>"42", "xml_views"=>"0", "html_views"=>"76"}
  • {"month"=>"2", "year"=>"2018", "pdf_views"=>"22", "xml_views"=>"0", "html_views"=>"60"}
  • {"month"=>"3", "year"=>"2018", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"45"}
  • {"month"=>"4", "year"=>"2018", "pdf_views"=>"25", "xml_views"=>"1", "html_views"=>"40"}
  • {"month"=>"5", "year"=>"2018", "pdf_views"=>"16", "xml_views"=>"0", "html_views"=>"44"}
  • {"month"=>"6", "year"=>"2018", "pdf_views"=>"29", "xml_views"=>"2", "html_views"=>"43"}
  • {"month"=>"7", "year"=>"2018", "pdf_views"=>"24", "xml_views"=>"4", "html_views"=>"41"}
  • {"month"=>"8", "year"=>"2018", "pdf_views"=>"10", "xml_views"=>"2", "html_views"=>"50"}
  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"34", "xml_views"=>"1", "html_views"=>"81"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"12", "xml_views"=>"1", "html_views"=>"42"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"39", "xml_views"=>"6", "html_views"=>"72"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"26"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"20", "xml_views"=>"0", "html_views"=>"45"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"43"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"26", "xml_views"=>"0", "html_views"=>"43"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"27", "xml_views"=>"0", "html_views"=>"67"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"14", "xml_views"=>"0", "html_views"=>"69"}
  • {"month"=>"6", "year"=>"2019", "pdf_views"=>"22", "xml_views"=>"0", "html_views"=>"60"}
  • {"month"=>"7", "year"=>"2019", "pdf_views"=>"36", "xml_views"=>"4", "html_views"=>"48"}
  • {"month"=>"8", "year"=>"2019", "pdf_views"=>"24", "xml_views"=>"0", "html_views"=>"39"}
  • {"month"=>"9", "year"=>"2019", "pdf_views"=>"30", "xml_views"=>"1", "html_views"=>"62"}
  • {"month"=>"10", "year"=>"2019", "pdf_views"=>"29", "xml_views"=>"1", "html_views"=>"87"}
  • {"month"=>"11", "year"=>"2019", "pdf_views"=>"47", "xml_views"=>"0", "html_views"=>"74"}
  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"47", "xml_views"=>"1", "html_views"=>"69"}
  • {"month"=>"1", "year"=>"2020", "pdf_views"=>"55", "xml_views"=>"0", "html_views"=>"45"}
  • {"month"=>"2", "year"=>"2020", "pdf_views"=>"14", "xml_views"=>"2", "html_views"=>"14"}
  • {"month"=>"3", "year"=>"2020", "pdf_views"=>"20", "xml_views"=>"0", "html_views"=>"23"}
  • {"month"=>"4", "year"=>"2020", "pdf_views"=>"19", "xml_views"=>"1", "html_views"=>"23"}
  • {"month"=>"5", "year"=>"2020", "pdf_views"=>"32", "xml_views"=>"1", "html_views"=>"17"}
  • {"month"=>"6", "year"=>"2020", "pdf_views"=>"41", "xml_views"=>"1", "html_views"=>"45"}
  • {"month"=>"7", "year"=>"2020", "pdf_views"=>"29", "xml_views"=>"0", "html_views"=>"55"}
  • {"month"=>"8", "year"=>"2020", "pdf_views"=>"28", "xml_views"=>"1", "html_views"=>"43"}
  • {"month"=>"9", "year"=>"2020", "pdf_views"=>"41", "xml_views"=>"1", "html_views"=>"72"}
  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"15", "xml_views"=>"1", "html_views"=>"52"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4246627"], "description"=>"<div><p>Background</p><p>Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.</p><p>Methods</p><p>The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.</p><p>Results</p><p>After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).</p><p>Conclusion</p><p>The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.</p></div>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598466, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195", "stats"=>{"downloads"=>5, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Fusing_Data_Mining_Machine_Learning_and_Traditional_Statistics_to_Detect_Biomarkers_Associated_with_Depression/2598466", "title"=>"Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246630"], "description"=>"<p>Hybrid Methodology Steps.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598469, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Hybrid_Methodology_Steps_/2598469", "title"=>"Hybrid Methodology Steps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246633"], "description"=>"<p>Estimated covariate statistics.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598472, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Estimated_covariate_statistics_/2598472", "title"=>"Estimated covariate statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246636"], "description"=>"<p>Boosted regression statistics.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598475, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Boosted_regression_statistics_/2598475", "title"=>"Boosted regression statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246642"], "description"=>"<p>Univariate Logistic Regression statistics.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598481, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Univariate_Logistic_Regression_statistics_/2598481", "title"=>"Univariate Logistic Regression statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246645"], "description"=>"<p>Final Four biomarkers from boosted regression.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598484, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Final_Four_biomarkers_from_boosted_regression_/2598484", "title"=>"Final Four biomarkers from boosted regression.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246648"], "description"=>"<p>Final Multivariate Logistic Regression.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598487, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Final_Multivariate_Logistic_Regression_/2598487", "title"=>"Final Multivariate Logistic Regression.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/4246651"], "description"=>"<p>Top 15 biomarkers selected from lasso regression.</p>", "links"=>[], "tags"=>["67 biomarkers", "alcohol use", "20 imputation data sets", "future hypothesis generation", "survey design", "serum glucose", "Body Mass Index", "Depression BackgroundAtheoretical", "depression", "21 biomarkers", "Detect Biomarkers Associated", "methodology amalgamating", "Poverty Income Ratio", "selection methodology", "regression model", "National Health", "cell distribution width", "regression algorithm", "food security", "Fusing Data Mining", "data mining techniques", "Traditional Statistics", "CI", "regression methods", "bilirubin", "survey sampling methodology", "regression sequences", "Machine Learning", "study.MethodsThe study"], "article_id"=>2598490, "categories"=>["Cell Biology", "Biotechnology", "Cancer", "Mental Health", "Infectious Diseases", "Virology"], "users"=>["Joanna F. Dipnall", "Julie A. Pasco", "Michael Berk", "Lana J. Williams", "Seetal Dodd", "Felice N. Jacka", "Denny Meyer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0148195.t006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Top_15_biomarkers_selected_from_lasso_regression_/2598490", "title"=>"Top 15 biomarkers selected from lasso regression.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-02-05 20:29:09"}

PMC Usage Stats | Further Information

  • {"unique-ip"=>"75", "full-text"=>"75", "pdf"=>"31", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
  • {"unique-ip"=>"40", "full-text"=>"44", "pdf"=>"25", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"3"}
  • {"unique-ip"=>"30", "full-text"=>"27", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
  • {"unique-ip"=>"21", "full-text"=>"22", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
  • {"unique-ip"=>"18", "full-text"=>"24", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
  • {"unique-ip"=>"25", "full-text"=>"23", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"7"}
  • {"unique-ip"=>"42", "full-text"=>"47", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
  • {"unique-ip"=>"34", "full-text"=>"44", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
  • {"unique-ip"=>"28", "full-text"=>"34", "pdf"=>"14", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"10"}
  • {"unique-ip"=>"39", "full-text"=>"38", "pdf"=>"13", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
  • {"unique-ip"=>"38", "full-text"=>"46", "pdf"=>"19", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
  • {"unique-ip"=>"32", "full-text"=>"27", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"1"}
  • {"unique-ip"=>"28", "full-text"=>"29", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
  • {"unique-ip"=>"23", "full-text"=>"24", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"3"}
  • {"unique-ip"=>"22", "full-text"=>"26", "pdf"=>"12", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"4"}
  • {"unique-ip"=>"31", "full-text"=>"36", "pdf"=>"19", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"5"}
  • {"unique-ip"=>"31", "full-text"=>"39", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}
  • {"unique-ip"=>"30", "full-text"=>"43", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"7"}
  • {"unique-ip"=>"23", "full-text"=>"27", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"8"}
  • {"unique-ip"=>"31", "full-text"=>"37", "pdf"=>"10", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"9"}
  • {"unique-ip"=>"31", "full-text"=>"31", "pdf"=>"8", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"10"}
  • {"unique-ip"=>"30", "full-text"=>"27", "pdf"=>"11", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2017", "month"=>"11"}
  • {"unique-ip"=>"27", "full-text"=>"30", "pdf"=>"9", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"12"}
  • {"unique-ip"=>"14", "full-text"=>"13", "pdf"=>"7", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"2"}
  • {"unique-ip"=>"35", "full-text"=>"31", "pdf"=>"14", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"26", "full-text"=>"27", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2019", "month"=>"1"}
  • {"unique-ip"=>"19", "full-text"=>"20", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"22", "full-text"=>"24", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"25", "full-text"=>"26", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
  • {"unique-ip"=>"23", "full-text"=>"24", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
  • {"unique-ip"=>"11", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"19", "full-text"=>"17", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"29", "full-text"=>"30", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"21", "full-text"=>"19", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"23", "full-text"=>"27", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"20", "full-text"=>"17", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"30", "full-text"=>"30", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"20", "full-text"=>"20", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"29", "full-text"=>"23", "pdf"=>"11", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"31", "full-text"=>"32", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"25", "full-text"=>"26", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"20", "full-text"=>"20", "pdf"=>"11", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"22", "full-text"=>"30", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
  • {"unique-ip"=>"61", "full-text"=>"64", "pdf"=>"11", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
  • {"unique-ip"=>"74", "full-text"=>"84", "pdf"=>"15", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"60", "full-text"=>"75", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"48", "full-text"=>"64", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"41", "full-text"=>"41", "pdf"=>"12", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"8", "full-text"=>"6", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"21", "full-text"=>"23", "pdf"=>"13", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"15", "full-text"=>"14", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
Loading … Spinner
There are currently no alerts