Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers
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{"title"=>"Robust selection algorithm (RSA) for multi-omic biomarker discovery; integration with functional network analysis to identify miRNA regulated pathways in multiple cancers", "type"=>"journal", "authors"=>[{"first_name"=>"Vasudha", "last_name"=>"Sehgal", "scopus_author_id"=>"55967935900"}, {"first_name"=>"Elena G.", "last_name"=>"Seviour", "scopus_author_id"=>"37040853600"}, {"first_name"=>"Tyler J.", "last_name"=>"Moss", "scopus_author_id"=>"35079201000"}, {"first_name"=>"Gordon B.", "last_name"=>"Mills", "scopus_author_id"=>"35379638300"}, {"first_name"=>"Robert", "last_name"=>"Azencott", "scopus_author_id"=>"6603604076"}, {"first_name"=>"Prahlad T.", "last_name"=>"Ram", "scopus_author_id"=>"7007153993"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "doi"=>"10.1371/journal.pone.0140072", "sgr"=>"84949579965", "scopus"=>"2-s2.0-84949579965", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"26505200", "pui"=>"607188248"}, "id"=>"1c3bf63e-973d-3302-aef9-ac77dbcf08e6", "abstract"=>"MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.", "link"=>"http://www.mendeley.com/research/robust-selection-algorithm-rsa-multiomic-biomarker-discovery-integration-functional-network-analysis", "reader_count"=>21, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>3, "Researcher"=>3, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Other"=>2, "Student > Bachelor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>3, "Researcher"=>3, "Student > Ph. D. Student"=>5, "Student > Master"=>3, "Other"=>2, "Student > Bachelor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>1, "Agricultural and Biological Sciences"=>4, "Medicine and Dentistry"=>1, "Computer Science"=>5, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>1}}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2380933"], "description"=>"<p>(A) miR-24-1* miRNA-mRNA interaction networks. Networks of positively (yellow) and inversely (blue) correlated mRNA and associated functions in BRCA, in which miR-24-1* is correlated with poor survival. (B) Common functions associated with the miRNA-mRNA correlation networks when miR-24-1* is correlated with good survival in three different cancer types. The log of the beta values in KIRC is displayed.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586019, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g005", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_mRNA_interaction_networks_for_miR_24_1_whose_functions_are_conserved_across_cancer_types_/1586019", "title"=>"miRNA-mRNA interaction networks for miR-24-1*, whose functions are conserved across cancer types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/2380956", "https://ndownloader.figshare.com/files/2380957", "https://ndownloader.figshare.com/files/2380958", "https://ndownloader.figshare.com/files/2380959", "https://ndownloader.figshare.com/files/2380960", "https://ndownloader.figshare.com/files/2380961", "https://ndownloader.figshare.com/files/2380962", "https://ndownloader.figshare.com/files/2380963", "https://ndownloader.figshare.com/files/2380964", "https://ndownloader.figshare.com/files/2380965", "https://ndownloader.figshare.com/files/2380967", "https://ndownloader.figshare.com/files/2380968", "https://ndownloader.figshare.com/files/2380971", "https://ndownloader.figshare.com/files/2380972", "https://ndownloader.figshare.com/files/2380973", "https://ndownloader.figshare.com/files/2380974", "https://ndownloader.figshare.com/files/2380975", "https://ndownloader.figshare.com/files/2380976", "https://ndownloader.figshare.com/files/2380977", "https://ndownloader.figshare.com/files/2380978", "https://ndownloader.figshare.com/files/2380979", "https://ndownloader.figshare.com/files/2380980", "https://ndownloader.figshare.com/files/2380981", "https://ndownloader.figshare.com/files/2380982", "https://ndownloader.figshare.com/files/2380983", "https://ndownloader.figshare.com/files/2380984", "https://ndownloader.figshare.com/files/2380985", "https://ndownloader.figshare.com/files/2380986", "https://ndownloader.figshare.com/files/2380987", "https://ndownloader.figshare.com/files/2380988", "https://ndownloader.figshare.com/files/2380989", "https://ndownloader.figshare.com/files/2380990"], "description"=>"<div><p>MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.</p></div>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586038, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. 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  • {"files"=>["https://ndownloader.figshare.com/files/2380919"], "description"=>"<p>(A) Candidate miRNAs from RSA significantly (robust p-value < 0.01) correlated with good survival or poor survival in at least 3 cancer types. (B) MiRNA-disease survival network. The circles indicate the miRNAs strongly linked with patient survival across diverse cancer types. Left to right: miRNAs linked to prognosis in one cancer type, 2 cancer types, and 3 cancer types. White rectangles represent cancer types. Yellow rectangles represent miRNAs. The color of the edge between a miRNA and a cancer type, indicates whether the miRNA is correlated with good (blue) or poor (orange) prognosis in a cancer type.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586007, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Candidate_miRNAs_significantly_correlated_with_survival_across_cancer_types_/1586007", "title"=>"Candidate miRNAs significantly correlated with survival across cancer types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/2380939"], "description"=>"<p>(A) Inversely correlated miRNA-mRNA network in BRCA showing conserved functions across 4 cancer types. (B) Positively correlated miRNA-mRNA network in BRCA showing conserved functions across 4 cancer types.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586024, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g006", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_mRNA_interaction_networks_for_miR_15b_whose_functions_are_conserved_across_cancer_types_/1586024", "title"=>"miRNA-mRNA interaction networks for miR-15b, whose functions are conserved across cancer types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/2380923"], "description"=>"<p>(A) Further characterization of the 5 strong candidate miRNAs in terms of copy number variation and expression. The GISTIC-identified copy number alterations at each of the chromosome loci for the miRNAs in different cancer types are displayed. The “GS” or “PS” inside each circle indicates the link with good (blue) or poor (orange) prognosis. (B) Expression in tumor and normal tissue for each of the strong candidate miRNA. For OVCA, the normal tissue data were not available.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586010, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g003", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characterization_of_miRNAs_found_to_be_strong_candidate_markers_of_prognosis_based_on_copy_number_variation_and_expression_/1586010", "title"=>"Characterization of miRNAs found to be strong candidate markers of prognosis based on copy number variation and expression.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/2380927"], "description"=>"<p>miR-487b miRNA-mRNA interaction networks. mRNA networks that were positively (yellow) or inversely (blue) correlated with miR-487b in OVCA and involved in functions conserved across cancer types are shown.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1586014, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g004", "stats"=>{"downloads"=>4, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_miRNA_mRNA_interaction_networks_for_miR_487b_whose_functions_are_conserved_across_cancer_types_/1586014", "title"=>"miRNA-mRNA interaction networks for miR-487b, whose functions are conserved across cancer types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/2380909"], "description"=>"<p>(A) Schematic displaying the overview of the RSA. The inputs are clinical data and miRNA expression data; the outcomes are candidate miRNAs correlated with either good or poor survival. (B) Validation of the RSA using previously published gene signatures correlated with survival outcomes. We applied RSA to breast cancer dataset in Martin et al. And looked at the overlap of genes correlated with good and poor survival computed by RSA and from their results. Heatmap of these overlapping genes was drawn displaying the high gene intensity in yellow and low gene intensity in blue.</p>", "links"=>[], "tags"=>["survival analysis", "Identify miRNA Regulated Pathways", "Multiple Cancers MicroRNAs", "cancer progression", "tumor suppressors", "rsa", "Robust Selection Algorithm", "survival analysis techniques", "Functional Network Analysis", "network analysis framework", "target genes", "identification", "omics expression", "Patient Outcome", "bioinformatic tools", "miRNA expression", "cutoff values", "data omics analysis", "biomarker"], "article_id"=>1585999, "categories"=>["Uncategorised"], "users"=>["Vasudha Sehgal", "Elena G. Seviour", "Tyler J. Moss", "Gordon B. Mills", "Robert Azencott", "Prahlad T. Ram"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0140072.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Workflow_of_our_robust_selection_algorithm_RSA_and_validation_of_the_RSA_using_previously_published_datasets_/1585999", "title"=>"Workflow of our robust selection algorithm (RSA) and validation of the RSA using previously published datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-27 02:58:29"}

PMC Usage Stats | Further Information

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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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