Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels
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{"title"=>"Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels", "type"=>"journal", "authors"=>[{"first_name"=>"Serdar", "last_name"=>"Bozdag", "scopus_author_id"=>"24472576200"}, {"first_name"=>"Aiguo", "last_name"=>"Li", "scopus_author_id"=>"35187250300"}, {"first_name"=>"Gregory", "last_name"=>"Riddick", "scopus_author_id"=>"16432575300"}, {"first_name"=>"Yuri", "last_name"=>"Kotliarov", "scopus_author_id"=>"12807078800"}, {"first_name"=>"Mehmet", "last_name"=>"Baysan", "scopus_author_id"=>"8386447300"}, {"first_name"=>"Fabio M.", "last_name"=>"Iwamoto", "scopus_author_id"=>"54383415100"}, {"first_name"=>"Margaret C.", "last_name"=>"Cam", "scopus_author_id"=>"6701537128"}, {"first_name"=>"Svetlana", "last_name"=>"Kotliarova", "scopus_author_id"=>"6602637499"}, {"first_name"=>"Howard A.", "last_name"=>"Fine", "scopus_author_id"=>"7006606728"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84876895426", "pmid"=>"23658659", "isbn"=>"9781450324342", "pui"=>"368833119", "issn"=>"19326203", "scopus"=>"2-s2.0-84876895426", "doi"=>"10.1371/journal.pone.0062982"}, "id"=>"9e18cd7c-cd79-3c38-b72f-c13fa812a6cf", "abstract"=>"Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.", "link"=>"http://www.mendeley.com/research/agespecific-signatures-glioblastoma-genomic-genetic-epigenetic-levels", "reader_count"=>50, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>2, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>8, "Medicine and Dentistry"=>15, "Agricultural and Biological Sciences"=>18, "Neuroscience"=>3, "Computer Science"=>3, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Neuroscience"=>{"Neuroscience"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>18}, "Computer Science"=>{"Computer Science"=>3}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Finland"=>1, "Ukraine"=>1, "Brazil"=>2, "Italy"=>1, "Germany"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1048097"], "description"=>"<p>Node color represents the expression status based on Affymetrix U133A data set (Red: Upregulated in old GBMs, Green: Downregulated in old GBMs, Gray: Baseline, White: Unknown, Mix of green and red: both upregulated and downregulated genes in the complex).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "angiogenesis-related"], "article_id"=>693602, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g007", "stats"=>{"downloads"=>14, "page_views"=>43, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_IPA_network_of_angiogenesis_related_genes_/693602", "title"=>"IPA network of angiogenesis-related genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 01:00:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048098"], "description"=>"1<p>Level 2 refers to probeset-level data and level 3 refers to gene-level data for expression and methylation data sets. Level 3 refers to segmented data for copy number and SNP data sets. There is no level number for whole exome sequence data set as we just used the mutations derived from this data set.</p>2<p>Old and Young refer to samples ≥70 and ≤40 years old, respectively.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "gbm", "samples", "TCGA", "repository", "june", "ids"], "article_id"=>693603, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_GBM_samples_used_in_this_study_downloaded_from_the_TCGA_repository_on_June_29_2011_Sample_IDs_are_in_Table_S1_/693603", "title"=>"Number of GBM samples used in this study (downloaded from the TCGA repository on June 29, 2011, Sample IDs are in Table S1).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-29 01:00:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048100"], "description"=>"1<p>Shows number of old of young samples each gene is mutated. For instance, there are 3038 genes that are mutated in at least one old sample (see first row) and PTEN is mutated in more than 15 old samples (see last row).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "mutated", "genes", "gneg"], "article_id"=>693605, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_mutated_genes_in_old_and_young_GNEG_GBMs_/693605", "title"=>"Number of mutated genes in old and young GNEG GBMs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-29 01:00:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048101"], "description"=>"<p>The last row shows the number of differentially expressed genes found in all three platforms.</p>1<p>In each test, FDR≤0.05 threshold is applied.</p>2<p>Shows the number of differentially expressed genes found in all three platforms.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "differentially", "genes", "gbm", "samples", "transcriptomic"], "article_id"=>693606, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.t002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_differentially_expressed_genes_between_Old_and_Young_GBM_samples_for_three_transcriptomic_platforms_/693606", "title"=>"Number of differentially expressed genes between Old and Young GBM samples for three transcriptomic platforms.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-04-29 01:00:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048103", "https://ndownloader.figshare.com/files/1048104", "https://ndownloader.figshare.com/files/1048106", "https://ndownloader.figshare.com/files/1048107", "https://ndownloader.figshare.com/files/1048108", "https://ndownloader.figshare.com/files/1048109", "https://ndownloader.figshare.com/files/1048110", "https://ndownloader.figshare.com/files/1048111", "https://ndownloader.figshare.com/files/1048113", "https://ndownloader.figshare.com/files/1048114", "https://ndownloader.figshare.com/files/1048115", "https://ndownloader.figshare.com/files/1048116", "https://ndownloader.figshare.com/files/1048117", "https://ndownloader.figshare.com/files/1048118", "https://ndownloader.figshare.com/files/1048119", "https://ndownloader.figshare.com/files/1048120", "https://ndownloader.figshare.com/files/1048121", "https://ndownloader.figshare.com/files/1048122", "https://ndownloader.figshare.com/files/1048123"], "description"=>"<div><p></p><p>Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "signatures", "glioblastoma", "epigenetic"], "article_id"=>693608, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0062982.s001", "https://dx.doi.org/10.1371/journal.pone.0062982.s002", "https://dx.doi.org/10.1371/journal.pone.0062982.s003", "https://dx.doi.org/10.1371/journal.pone.0062982.s004", "https://dx.doi.org/10.1371/journal.pone.0062982.s005", "https://dx.doi.org/10.1371/journal.pone.0062982.s006", "https://dx.doi.org/10.1371/journal.pone.0062982.s007", "https://dx.doi.org/10.1371/journal.pone.0062982.s008", "https://dx.doi.org/10.1371/journal.pone.0062982.s009", "https://dx.doi.org/10.1371/journal.pone.0062982.s010", "https://dx.doi.org/10.1371/journal.pone.0062982.s011", "https://dx.doi.org/10.1371/journal.pone.0062982.s012", "https://dx.doi.org/10.1371/journal.pone.0062982.s013", "https://dx.doi.org/10.1371/journal.pone.0062982.s014", "https://dx.doi.org/10.1371/journal.pone.0062982.s015", "https://dx.doi.org/10.1371/journal.pone.0062982.s016", "https://dx.doi.org/10.1371/journal.pone.0062982.s017", "https://dx.doi.org/10.1371/journal.pone.0062982.s018", "https://dx.doi.org/10.1371/journal.pone.0062982.s019"], "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Age_Specific_Signatures_of_Glioblastoma_at_the_Genomic_Genetic_and_Epigenetic_Levels_/693608", "title"=>"Age-Specific Signatures of Glioblastoma at the Genomic, Genetic, and Epigenetic Levels", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-04-29 01:00:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048073"], "description"=>"<p>Red: G-CIMP negative, Blue: G-CIMP positive. Methylation sites with std. deviation >0.2 are selected to generate this graph.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "gbm", "samples", "methylation"], "article_id"=>693582, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g001", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PCA_plot_of_GBM_samples_with_methylation_data_/693582", "title"=>"PCA plot of GBM samples with methylation data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048075"], "description"=>"<p>Kaplan-Meier plot between old, young, and middle-aged GBM samples.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "middle-aged", "gbm"], "article_id"=>693584, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g002", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kaplan_Meier_plot_between_old_young_and_middle_aged_GBM_samples_/693584", "title"=>"Kaplan-Meier plot between old, young, and middle-aged GBM samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048078"], "description"=>"<p>Data are from (a) Agilent Human Genome CGH Microarray 244A (Memorial Sloan-Kettering Cancer Center), (b) Affymetrix Genome-Wide Human SNP Array 6.0 (Broad Institute of MIT and Harvard), (c) Illumina 550 K Infinium HumanHap550 SNP Chip (HudsonAlpha Institute for Biotechnology) platforms (chr 1–23). Green bars represent amplification and red bars represent deletion. The height of each bar represents the frequency of the alteration in the group. The differentially amplified genes are in chromosome 7 and differentially deleted genes are in chromosome 10.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "alteration", "profiles", "gbm"], "article_id"=>693586, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_wide_copy_number_alteration_profiles_of_old_and_young_GBM_samples_/693586", "title"=>"Genome-wide copy number alteration profiles of old and young GBM samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048082"], "description"=>"<p>(A) Clustering of old and young REMBRANDT GBM samples based on the expression profiles of age-specific genes derived from both TCGA Affymetrix U133A and Agilent G4502A data sets. (B) Clustering of old and young REMBRANDT GBM samples based on the expression profiles of all genes in the REMBRANDT data set. (C) Clustering of the old and young TCGA GBM samples based on the expression profiles of age-specific genes derived from both TCGA Affymetrix U133A and Agilent G4502A data sets.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "clustering", "gbm", "samples", "rembrandt", "TCGA"], "article_id"=>693587, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g004", "stats"=>{"downloads"=>3, "page_views"=>58, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_of_GBM_samples_in_the_REMBRANDT_and_TCGA_data_sets_/693587", "title"=>"Hierarchical clustering of GBM samples in the REMBRANDT and TCGA data sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048091"], "description"=>"<p>The x-axis shows the components of the 3-way ANOVA model and the y-axis shows the median signal to noise ratio. The ANOVA model is built based on the expression profiles of the TCGA age-specific genes in REMBRANDT data set. The TCGA age-specific genes are the intersection of DEGs computed on TCGA Affymetrix U133A and Agilent G4502A data sets.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "profiles", "age-specific", "genes", "rembrandt"], "article_id"=>693596, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Source_of_variation_of_expression_profiles_of_age_specific_genes_in_the_REMBRANDT_data_set_/693596", "title"=>"Source of variation of expression profiles of age-specific genes in the REMBRANDT data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/1048094"], "description"=>"<p>The x-axis shows the components of the 3-way ANOVA model and the y-axis shows the median signal to noise ratio. The ANOVA model is built based on the expression profiles of all genes in REMBRANDT data set.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene ontologies", "Genome expression analysis", "microarrays", "genetics", "epigenetics", "oncology", "Cancer risk factors", "Aging and cancer", "Cancers and neoplasms", "Neurological tumors", "Glioblastoma multiforme", "profiles", "genes", "rembrandt"], "article_id"=>693599, "categories"=>["Medicine", "Biological Sciences"], "users"=>["Serdar Bozdag", "Aiguo Li", "Gregory Riddick", "Yuri Kotliarov", "Mehmet Baysan", "Fabio M. Iwamoto", "Margaret C. Cam", "Svetlana Kotliarova", "Howard A. Fine"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0062982.g006", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Source_of_variation_of_expression_profiles_of_all_genes_in_the_REMBRANDT_data_set_/693599", "title"=>"Source of variation of expression profiles of all genes in the REMBRANDT data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-04-29 00:59:59"}

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Relative Metric

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