A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data
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{"title"=>"A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data", "type"=>"journal", "authors"=>[{"first_name"=>"Junhee", "last_name"=>"Seok", "scopus_author_id"=>"24069490100"}, {"first_name"=>"Ronald W.", "last_name"=>"Davis", "scopus_author_id"=>"36012646500"}, {"first_name"=>"Wenzhong", "last_name"=>"Xiao", "scopus_author_id"=>"55658056830"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84929091995", "pui"=>"604276317", "doi"=>"10.1371/journal.pone.0122103", "pmid"=>"25933378", "scopus"=>"2-s2.0-84929091995", "issn"=>"19326203"}, "id"=>"e1cf49c1-858b-3ffc-96b7-9e74135ace95", "abstract"=>"Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn't been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.", "link"=>"http://www.mendeley.com/research/hybrid-approach-gene-sets-single-genes-prediction-survival-risks-gene-expression-data-1", "reader_count"=>7, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>3, "Other"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>3, "Other"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>2, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2048986"], "description"=>"<p>The Kaplan-Meier curves for the recovery of high-risk (solid) and low-risk (dashed) patients according to the recovery risk predicted <b>(A)</b> by the conventional method with only single genes and <b>(B)</b> by the proposed hybrid method using both single genes and gene sets.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401549, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.g004", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_performance_for_the_trauma_benchmark_data_set_/1401549", "title"=>"Prediction performance for the trauma benchmark data set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048985"], "description"=>"<p>Shown are the prediction performance of the proposed hybrid method using both gene sets and single genes for various gene set collections (TR, C1-4, and IS). The prediction performance with only single genes are also shown as a reference (SG). Subplots are for <b>(A)</b> likelihood ratio of Cox proportional hazard model fitting, <b>(B)</b> Harrell’s C index, <b>(C)</b> R<sup>2</sup>, and <b>(D)</b> the log-rank test p-value when stratified in the median. Dashed lines represent the median statistics of single gene predictions.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401548, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.g003", "stats"=>{"downloads"=>6, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_performance_of_the_proposed_hybrid_method_/1401548", "title"=>"Prediction performance of the proposed hybrid method.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048990", "https://ndownloader.figshare.com/files/2048991"], "description"=>"<div><p>Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.</p></div>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401553, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0122103.s001", "https://dx.doi.org/10.1371/journal.pone.0122103.s002"], "stats"=>{"downloads"=>14, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Hybrid_Approach_of_Gene_Sets_and_Single_Genes_for_the_Prediction_of_Survival_Risks_with_Gene_Expression_Data_/1401553", "title"=>"A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048988"], "description"=>"<p>Benchmark data sets used in the study.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401551, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.t002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmark_data_sets_used_in_the_study_/1401551", "title"=>"Benchmark data sets used in the study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048987"], "description"=>"<p>Gene set collections used in the study.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401550, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.t001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_set_collections_used_in_the_study_/1401550", "title"=>"Gene set collections used in the study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048984"], "description"=>"<p>Shown are the prediction performance with TR, C1-4, and IS gene set collections as well as with only single genes (SG). Subplots are for <b>(A)</b> likelihood ratio of Cox proportional hazard model fitting, <b>(B)</b> Harrell’s C index, <b>(C)</b> R<sup>2</sup>, and <b>(D)</b> the log-rank test p-value when stratified in the median. Dashed lines represent the median statistics of single gene predictions.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401547, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.g002", "stats"=>{"downloads"=>4, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_performance_of_various_gene_set_collections_and_single_genes_/1401547", "title"=>"Prediction performance of various gene set collections and single genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-01 04:00:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/2048983"], "description"=>"<p><b>(A)</b> Shown are the rank correlation coefficients of single gene and gene set feature scores between two exclusive subsets of the seven benchmark data sets. For each data set, its training and test sets were used as exclusive subsets. <b>(B)</b> The top 20 predictive gene sets in the trauma benchmark data set are presented. Prediction powers were measured by feature scores. More predictive single genes have lower rank values. Gene sets are noted on the y-axis, and the distributions of their member genes' ranks are plotted along x-axis in a log scale. For each gene set, the left-end of its boxplot represents the highest rank of its member single genes, the right-end represents the lowest rank, and the bar in the middle represents the median rank.</p>", "links"=>[], "tags"=>["survival prediction problems", "trauma injury data", "gene expression profiles", "patient survival risks", "Gene Expression Data Accumulated", "gene sets"], "article_id"=>1401546, "categories"=>["Biological Sciences"], "users"=>["Junhee Seok", "Ronald W. Davis", "Wenzhong Xiao"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0122103.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_single_gene_and_gene_set_features_/1401546", "title"=>"Comparison of single gene and gene set features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-01 04:00:45"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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