Combining Gene Signatures Improves Prediction of Breast Cancer Survival
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{"title"=>"Combining gene signatures improves prediction of breast cancer survival", "type"=>"journal", "authors"=>[{"first_name"=>"Xi", "last_name"=>"Zhao", "scopus_author_id"=>"57190420660"}, {"first_name"=>"Einar Andreas", "last_name"=>"Rødland", "scopus_author_id"=>"6506672322"}, {"first_name"=>"Therese", "last_name"=>"Sørlie", "scopus_author_id"=>"7004332362"}, {"first_name"=>"Bjørn", "last_name"=>"Naume", "scopus_author_id"=>"7003973772"}, {"first_name"=>"Anita", "last_name"=>"Langerød", "scopus_author_id"=>"7801324778"}, {"first_name"=>"Arnoldo", "last_name"=>"Frigessi", "scopus_author_id"=>"6602742543"}, {"first_name"=>"Vessela N.", "last_name"=>"Kristensen", "scopus_author_id"=>"7003526321"}, {"first_name"=>"Anne Lise", "last_name"=>"Børresen-Dale", "scopus_author_id"=>"7005465444"}, {"first_name"=>"Ole Christian", "last_name"=>"Lingjærde", "scopus_author_id"=>"35446149600"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-79952502399", "sgr"=>"79952502399", "pui"=>"361419326", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pmid"=>"21423775", "doi"=>"10.1371/journal.pone.0017845"}, "id"=>"8b132556-7f3f-3382-be65-616938fe0f89", "abstract"=>"BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.\\n\\nPRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.\\n\\nCONCLUSION: Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.", "link"=>"http://www.mendeley.com/research/combining-gene-signatures-improves-prediction-breast-cancer-survival-7", "reader_count"=>38, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>6, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Ph. D. Student"=>8, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>6, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>16, "Medicine and Dentistry"=>7, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United States"=>1, "France"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/396351", "https://ndownloader.figshare.com/files/396367", "https://ndownloader.figshare.com/files/396378", "https://ndownloader.figshare.com/files/396385", "https://ndownloader.figshare.com/files/396395", "https://ndownloader.figshare.com/files/396405", "https://ndownloader.figshare.com/files/396416", "https://ndownloader.figshare.com/files/396423", "https://ndownloader.figshare.com/files/396429", "https://ndownloader.figshare.com/files/396439"], "description"=>"<div><h3>Background</h3><p>Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.</p> <h3>Principal Findings</h3><p>To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, <em>TP53</em> mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: <em>p</em> = 0.005; breast cancer death: <em>p</em> = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: <em>p</em> = 0.003; breast cancer death: <em>p</em> = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.</p> <h3>Conclusion</h3><p>Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.</p> </div>", "links"=>[], "tags"=>["combining", "signatures", "improves", "cancer"], "article_id"=>138297, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.s001", "https://dx.doi.org/10.1371/journal.pone.0017845.s002", "https://dx.doi.org/10.1371/journal.pone.0017845.s003", "https://dx.doi.org/10.1371/journal.pone.0017845.s004", "https://dx.doi.org/10.1371/journal.pone.0017845.s005", "https://dx.doi.org/10.1371/journal.pone.0017845.s006", "https://dx.doi.org/10.1371/journal.pone.0017845.s007", "https://dx.doi.org/10.1371/journal.pone.0017845.s008", "https://dx.doi.org/10.1371/journal.pone.0017845.s009", "https://dx.doi.org/10.1371/journal.pone.0017845.s010"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Combining_Gene_Signatures_Improves_Prediction_of_Breast_Cancer_Survival/138297", "title"=>"Combining Gene Signatures Improves Prediction of Breast Cancer Survival", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-10 02:18:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/791783"], "description"=>"<p>(<b>A</b>) Construction of the gene-set predictor/gene signature for risk prediction. Input: A set of genes of interest (gene <i>1</i>, …, <i>m</i>) which can be traced by the corresponding colors through out the diagram; gene expression data for training cohort and test cohort with genes placed in the rows and patients in the columns. <i>Step 1</i>. Gene identity mapping and extract expression matrix. <i>Step 2</i>. With available status of observing an event for the patients on the training set, a Cox model with L2 penalty is used to model the relationship of survival probability and gene expression pattern of the gene set. The coefficients or “gene weights” (<i>β<sub>1</sub>, …, β<sub>m</sub></i>) associated with individual genes are estimated from the Cox-ridge model. Size of the bubble in the gene weights matrix reflects the importance of the corresponding gene for survival prediction. <i>Step 3</i>. A <i>Prognostic Index</i> (PI), the predicted risk score for a test patient <i>i</i> (<i>i = 1, …, n</i>) is calculated by the sum of weighted gene expression from test patient <i>i</i> using the estimated gene weights from step2. (<b>B</b>) Integration of multiple gene signatures by dimension reduction. Input multiple gene sets of interest together with their gene expression data. <i>Module 1</i>: For <i>j</i>th gene set (<i>j</i> = 1, <i>…, R</i>), the procedure described in panel A is used to predict a risk score PI for individual test patient. The resulting PI matrix is positioned in <i>R</i> by <i>n</i> dimension representing the risk prediction of the <i>n</i> test patients by each of the <i>R</i> gene sets. <i>Module 2:</i> Integrate predictions from multiple gene signatures by dimension reduction using principal components analysis (PCA). <i>Module 3:</i> Dichotomize the risk scores on PC1 by median (higher than median indicates high risk) resulting in two predicted risk groups for survival outcome.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "oncology", "mathematics", "Non-clinical medicine"], "article_id"=>462135, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flowchart_of_the_analysis_/462135", "title"=>"Flowchart of the analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:35:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/791896"], "description"=>"<p>(<b>A</b>) Systemic recurrence: The figure shows that the predicted PIs across all the studied gene sets were roughly centered around 0, resulting from the standardization procedure of the expression matrix on both training and test set for individual gene set in the model building stage. The standard deviations of PIs for individual gene set are following: RS: 0.109; SD: 0; LM: 0; AMST: 0.195; ROT: 0.095; Grade: 0.078; Robust: 0.121; Hypoxia: 0.245; Stem: 0.044; Intrinsic: 0.137; WR: 0.037. Due to lack of convergence, the predicted PIs by gene set SD and LM was calculated by setting tuning parameter <i>λ,</i> at a large value. (<b>B</b>) Breast cancer specific death (BC specific death): Boxplot of predicted PIs on test set. Gene set LM failed to converge in the model training.</p>", "links"=>[], "tags"=>["pis"], "article_id"=>462256, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Boxplot_of_predicted_PIs_on_test_data_/462256", "title"=>"Boxplot of predicted PIs on test data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:37:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/792049"], "description"=>"<p>Results for systemic recurrence are in (A–B); for BC specific death in (C–D). In heatmaps (A, C), rows are notations for the gene sets. Columns are annotation for the patients; data outside of 1% quantile were trimmed. “Average” linkage based on Spearman correlation was used to construct the dendrograms. Figure A and C share legends for the clinical parameters. (<b>A</b>) Heatmap of predicted PIs on the test set for systemic recurrence from each gene sets. Two risk groups were observed from the hierarchical clustering; cluster I and cluster II. The control sample in Ull DNR_N_100, marked by green, was classified in the cluster associated with a lower risk (cluster II). (<b>B</b>) The Kaplan-Meier curves for the two clusters. A significant separation between the two groups was observed (χ<sup>2</sup> = 7.8, df = 1, <i>p</i> = 0.005). (<b>C</b>) Heatmap of predicted PIs on the test set for BC specific death from each gene sets. Two risk groups were observed from the hierarchical clustering; cluster I and cluster II. The control sample in Ull DNR_N_100, marked by green, was classified in the cluster associated with a lower risk (cluster I). (<b>D</b>) A significant separation between the two Kaplan-Meier curves associated with the clusters was observed (χ<sup>2</sup> = 5.996, df = 1, <i>p</i> = 0.014).</p>", "links"=>[], "tags"=>["clustering", "pis", "kaplan-meier"], "article_id"=>462409, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_of_predicted_PIs_on_test_set_and_Kaplan_Meier_analysis_of_the_clusters_/462409", "title"=>"Hierarchical clustering of predicted PIs on test set and Kaplan-Meier analysis of the clusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:40:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/792164"], "description"=>"<p>Heatmap of Spearman correlation matrix of predicted PIs for corresponding survival endpoint from individual gene sets. (<b>A</b>) For systemic recurrence, nine gene sets that reached convergence during modeling building are displayed. (<b>B</b>) For BC specific death, ten gene sets that reached convergence during modeling building are displayed. Figure A and B share the same color legend.</p>", "links"=>[], "tags"=>["pis", "sets", "convergence", "model-building"], "article_id"=>462523, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_structure_of_predicted_PIs_from_gene_sets_with_convergence_in_model_building_stage_/462523", "title"=>"Correlation structure of predicted PIs from gene sets with convergence in model-building stage.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:42:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/792225"], "description"=>"<p>The Kaplan-Meier curves and the associated logrank <i>p</i> values for dichotomized PI-risk groups from each of the 9 converged gene-set models.</p>", "links"=>[], "tags"=>["kaplan-meier", "pi-risk", "groups"], "article_id"=>462586, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Systemic_recurrence_Kaplan_Meier_plot_of_the_PI_risk_groups_for_each_of_the_individual_gene_sets_/462586", "title"=>"Systemic recurrence: Kaplan-Meier plot of the PI-risk groups for each of the individual gene sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:43:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/792323"], "description"=>"<p>Results for systemic recurrence are in (A-B); for BC specific death in (C-D). (A) Scatter plot of predicted PIs from 9 converged gene-set models on the space of the top two leading PCs. Black circles indicate censored observations; red dots indicate patients with relapse. (B) The Kaplan-Meier curves for high and low risk groups are significantly different (χ<sup>2</sup> = 8.76, df = 1, <i>p</i> = 0.003). (C) Scatter plot of predicted PIs from 10 converged gene-set models on the space of the top two leading PCs. Black circles indicate censored observations; red dots indicate patients with BC specific death; brown stars indicate death from other reasons. (D) The Kaplan-Meier curves for high and low risk groups are significantly different (χ<sup>2</sup> = 10.26, df = 1, <i>p</i> = 0.001).</p>", "links"=>[], "tags"=>["pca", "pis", "converged", "sets", "resulting", "groups"], "article_id"=>462675, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Systemic_recurrence_PCA_of_predicted_PIs_from_converged_gene_sets_and_performance_of_the_resulting_groups_from_PC1_/462675", "title"=>"Systemic recurrence: PCA of predicted PIs from converged gene sets and performance of the resulting groups from PC1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:44:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/792413"], "description"=>"<p>Comparison of combined-PI risk predictor with clinical parameters and individual gene-set predictors using univariate Cox model. (<b>A</b>) Y axis indicates C-index associated with individual predictor and X axis indicates the p values (on minus log10 scale) from likelihood ratio test in univariate Cox model. C-index  = 0.5 and the significant level: α = 0.05 for the likelihood ratio test are indicated by the dotted line. The size and the color of the bubble indicate the PVE and the deviance in univariate Cox model, respectively. The combined-PI risk predictor had the most significant marginal effect for predicting systemic recurrence (<i>p</i> = 0.003). It was associated with the second highest C-index score (C = 0.75) following <i>TP53</i> mutation status (C = 0.76). It had the second highest deviance (8.61) following tumor size (9.36), and the combined-PI predictor alone explained 10.6% of the variability as indicated by PVE, following tumor size (11.7%) and stage (11.1%) (<b>B</b>) X axis indicates HR from the univariate Cox model and the 95% CIs are shown along with the point estimates. “LR test” stands for likelihood ratio test. Insignificant predictors (likelihood ratio test <i>p</i>>0.05) are grayed out. To keep the results interpretable, only predictors with two levels are compared. The combined-PI risk predictor had the 2<sup>nd</sup> largest HR (2.82 with 95% CI 1.37—5.80) following <i>TP53</i> mutation status (2.87 with 95% CI 1.42—5.83).</p>", "links"=>[], "tags"=>["predictors", "systemic"], "article_id"=>462779, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Univariate_comparison_of_predictors_for_systemic_recurrence_/462779", "title"=>"Univariate comparison of predictors for systemic recurrence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:46:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/792499"], "description"=>"<p>For overall effect of the predictor in univariate Cox regression, Likelihood ratio test p value was reported. For individual levels within the predictor, Wald test <i>p</i> value was reported.</p><p>*PVE: proportion of variation explained in the outcome variable.</p>§<p>C: concordance index.</p>", "links"=>[], "tags"=>["pi", "predictor", "univariate", "multivariate", "cox"], "article_id"=>462855, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PCA_combined_PI_risk_predictor_in_univariate_and_multivariate_Cox_regression_/462855", "title"=>"PCA-combined PI risk predictor in univariate and multivariate Cox regression.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:47:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/792548"], "description"=>"<p>Clinical and molecular characteristics of the two risk groups from hierarchical clustering of the test patients based on the predicted PI matrix.</p>", "links"=>[], "tags"=>["molecular", "characteristics", "groups", "hierarchical", "clustering", "patients", "pi"], "article_id"=>462898, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clinical_and_molecular_characteristics_of_the_two_risk_groups_from_hierarchical_clustering_of_the_test_patients_based_on_the_predicted_PI_matrix_/462898", "title"=>"Clinical and molecular characteristics of the two risk groups from hierarchical clustering of the test patients based on the predicted PI matrix.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:48:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/792585"], "description"=>"<p>Individual gene set prediction characteristics (optimal <i>λ</i> by LOOCV in model building, change in deviance on test set, standard deviation for PIs).</p>", "links"=>[], "tags"=>["characteristics", "loocv", "deviance", "deviation"], "article_id"=>462945, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individual_gene_set_prediction_characteristics_optimal_955_by_LOOCV_in_model_building_change_in_deviance_on_test_set_standard_deviation_for_PIs_/462945", "title"=>"Individual gene set prediction characteristics (optimal <i>λ</i> by LOOCV in model building, change in deviance on test set, standard deviation for PIs).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:49:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/792623"], "description"=>"§<p>There was 1 gene overlapping between ROT & AMST according to the previous report <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017845#pone.0017845-Yu1\" target=\"_blank\">[15]</a>: <i>CCNE2</i> (GenBankID NM_004702). However, in the newer version of the NCBI database: “NM_004702.2 was permanently suppressed because the transcript has insufficient support and is a nonsense-mediated mRNA decay (NMD) candidate.” (<a href=\"http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&id=17318566\" target=\"_blank\">http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&id=17318566</a>. Accessed on Mar. 18, 2009).</p>", "links"=>[], "tags"=>["overlapping", "genes"], "article_id"=>462990, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_overlapping_genes_between_gene_sets_/462990", "title"=>"Number of overlapping genes between gene sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:49:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/792657"], "description"=>"§<p>128 Affymetrix probe IDs were used instead of 97 gene symbols.</p>‡<p>573 image clone IDs were used instead of 512 genes.</p>", "links"=>[], "tags"=>["sets"], "article_id"=>463020, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Acronyms_for_original_gene_sets_and_coverage_on_training_amp_test_set_/463020", "title"=>"Acronyms for original gene sets and coverage on training & test set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:50:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/792691"], "description"=>"<p>Published gene sets included in the analysis.</p>", "links"=>[], "tags"=>["sets", "included"], "article_id"=>463055, "categories"=>["Cancer", "Mathematics", "Biological Sciences", "Medicine", "Genetics"], "users"=>["Xi Zhao", "Einar Andreas Rødland", "Therese Sørlie", "Bjørn Naume", "Anita Langerød", "Arnoldo Frigessi", "Vessela N. Kristensen", "Anne-Lise Børresen-Dale", "Ole Christian Lingjærde"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0017845.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Published_gene_sets_included_in_the_analysis_/463055", "title"=>"Published gene sets included in the analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-03-10 00:50:55"}

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Relative Metric

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