A Model-Based Method for Gene Dependency Measurement
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{"title"=>"A model-based method for gene dependency measurement", "type"=>"journal", "authors"=>[{"first_name"=>"Qing", "last_name"=>"Zhang", "scopus_author_id"=>"56384720600"}, {"first_name"=>"Xiaodan", "last_name"=>"Fan", "scopus_author_id"=>"35302470300"}, {"first_name"=>"Yejun", "last_name"=>"Wang", "scopus_author_id"=>"37023750400"}, {"first_name"=>"Mingan", "last_name"=>"Sun", "scopus_author_id"=>"56152647400"}, {"first_name"=>"Samuel S.M.", "last_name"=>"Sun", "scopus_author_id"=>"35275988700"}, {"first_name"=>"Dianjing", "last_name"=>"Guo", "scopus_author_id"=>"24780473500"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"365284961", "doi"=>"10.1371/journal.pone.0040918", "sgr"=>"84864071483", "scopus"=>"2-s2.0-84864071483", "isbn"=>"1932-6203 (Electronic) 1932-6203 (Linking)", "pmid"=>"22829898"}, "id"=>"6e75e9a1-6fb7-3fe4-9264-4ab51930b524", "abstract"=>"<p>Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method–DBoMM (Difference in BIC of Mixture Models)–for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall’s tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using <italic>Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster</italic>, <italic>Arabidopsis thaliana</italic> data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 <italic>Escherichia coli</italic> regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.</p>", "link"=>"http://www.mendeley.com/research/modelbased-method-gene-dependency-measurement", "reader_count"=>19, "reader_count_by_academic_status"=>{"Researcher"=>7, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1, "Professor > Associate Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>7, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>1, "Professor > Associate Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>11, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Hong Kong"=>1, "United States"=>3, "Norway"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/606697"], "description"=>"<p>(a). <i>E.coli</i> dataset and the reference network from RegulonDB; (b). <i>Yeast</i> datset and the reference network from YEASTRACT; (c). <i>Drosophila</i> dataset and the reference network from DroID; (d). <i>Arabidopsis</i> datset and the reference network from AGRIS. X axis: recall; Y axis: precision. In general, DBoMM out-performs other 4 methods using various datasets.</p>", "links"=>[], "tags"=>["methods"], "article_id"=>277184, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040918.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_different_methods_using_PR_curve_/277184", "title"=>"A comparison of different methods using PR-curve.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 01:59:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/316738", "https://ndownloader.figshare.com/files/316756", "https://ndownloader.figshare.com/files/316776", "https://ndownloader.figshare.com/files/316822", "https://ndownloader.figshare.com/files/316855", "https://ndownloader.figshare.com/files/316888"], "description"=>"<div><p>Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method–DBoMM (Difference in BIC of Mixture Models)–for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall’s tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using <em>Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster</em>, <em>Arabidopsis thaliana</em> data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 <em>Escherichia coli</em> regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.</p> </div>", "links"=>[], "tags"=>["model-based", "dependency"], "article_id"=>122508, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0040918.s001", "https://dx.doi.org/10.1371/journal.pone.0040918.s002", "https://dx.doi.org/10.1371/journal.pone.0040918.s003", "https://dx.doi.org/10.1371/journal.pone.0040918.s004", "https://dx.doi.org/10.1371/journal.pone.0040918.s005", "https://dx.doi.org/10.1371/journal.pone.0040918.s006"], "stats"=>{"downloads"=>12, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Model_Based_Method_for_Gene_Dependency_Measurement/122508", "title"=>"A Model-Based Method for Gene Dependency Measurement", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-19 00:41:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/606975"], "description"=>"<p>The experimental conditions are classified into 6 different clusters based on the expression profiles of two genes (<i>lexA</i> and <i>recA</i>). Cn represents the index of the cluster.</p>", "links"=>[], "tags"=>["conditional", "interactions"], "article_id"=>277466, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040918.g004", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DBoMM_can_identify_the_conditional_dependent_regulatory_interactions_between_two_genes_/277466", "title"=>"DBoMM can identify the conditional dependent regulatory interactions between two genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:04:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/607278"], "description"=>"<p>The distributions of different similarity scores.</p>", "links"=>[], "tags"=>["distributions"], "article_id"=>277771, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040918.t001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distributions_of_different_similarity_scores_/277771", "title"=>"The distributions of different similarity scores.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-07-19 02:09:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/606906"], "description"=>"<p>Different levels of noise are introduced to the datasets. The numbers in the legend correspond to the noise levels, e.g. “noisy2” means 20% of noise introduced. DBoMM remains stable with up to 60% of noise. X axis: recall; y axis: precision.</p>", "links"=>[], "tags"=>["robust"], "article_id"=>277400, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040918.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DBoMM_is_robust_to_noise_/277400", "title"=>"DBoMM is robust to noise.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:03:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/606822"], "description"=>"<p>(a). The regulatory motif detected in the promoters of the 19 inferred target operons(upper) compared to the motif identified in PRODORIC. (b). The regulatory motif detected in the promoters of 8 inferred target operons(upper) compared to the motif identified in PRODORIC(lower).</p>", "links"=>[], "tags"=>["tf"], "article_id"=>277313, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Genetics"], "users"=>["Qing Zhang", "Xiaodan Fan", "Yejun Wang", "Mingan Sun", "Samuel S. M. Sun", "Dianjing Guo"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040918.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Motifs_detected_for_TF_and_/277313", "title"=>"Motifs detected for TF and .", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-19 02:01:53"}
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PMC Usage Stats | Further Information

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Relative Metric

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