CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks
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{"title"=>"CaSPIAN: A causal compressive sensing algorithm for discovering directed interactions in gene networks", "type"=>"journal", "authors"=>[{"first_name"=>"Amin", "last_name"=>"Emad", "scopus_author_id"=>"34881598200"}, {"first_name"=>"Olgica", "last_name"=>"Milenkovic", "scopus_author_id"=>"55946153300"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84897974178", "pmid"=>"24622336", "sgr"=>"84897974178", "doi"=>"10.1371/journal.pone.0090781", "isbn"=>"1932-6203", "issn"=>"19326203", "pui"=>"372799666", "arxiv"=>"1202.5678"}, "id"=>"ef5642e4-d850-38bf-96ce-e4dd26104401", "abstract"=>"We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered.", "link"=>"http://www.mendeley.com/research/caspian-causal-compressive-sensing-algorithm-discovering-directed-interactions-gene-networks", "reader_count"=>35, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>7, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>4, "Student > Bachelor"=>1, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>4, "Researcher"=>7, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>3, "Student > Master"=>4, "Other"=>4, "Student > Bachelor"=>1, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>5, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>1, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Computer Science"=>9}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>5}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"United States"=>2, "Brazil"=>1, "United Kingdom"=>1, "Germany"=>1, "India"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1417082"], "description"=>"<p>Parameters of this algorithm were chosen as and and average gene expressions were used. Solid arrows denote true-positives and dashed arrows denote false-positives. Green solid arrows correspond to the incorrect edges in the subnetwork. Precision is denoted by , sensitivity is denoted by , accuracy by , and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "corresponding", "irma", "applying", "algorithm", "incorrect"], "article_id"=>959584, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_network_corresponding_to_IRMA_obtained_by_applying_Algorithm_3_to_various_incorrect_subnetworks_/959584", "title"=>"The network corresponding to IRMA obtained by applying Algorithm 3 to various incorrect subnetworks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417080"], "description"=>"<p>Parameters of this algorithm were chosen as and and average gene expressions were used. Solid arrows denote true-positives and dashed arrows denote false-positives. Precision is denoted by , sensitivity by , accuracy by , and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "corresponding", "irma", "applying", "algorithm"], "article_id"=>959582, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_network_corresponding_to_IRMA_obtained_by_applying_Algorithm_3_to_various_subnetworks_/959582", "title"=>"The network corresponding to IRMA obtained by applying Algorithm 3 to various subnetworks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417077"], "description"=>"<p>Black solid arrows correspond to true positives and red dashed arrows correspond to false-positives. The numbers above edges describe their multiplicity ratios (MRs); in order to avoid cluttering, we did not plot the MRs for the results of Algorithm 1. Note that only links with MR at least are shown. Precision is denoted by , sensitivity by , accuracy by and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "sos", "reconstructed", "algorithm"], "article_id"=>959579, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_SOS_network_reconstructed_using_Lasso_TLasso_Algorithm_1_List_SP_and_CaSPIAN_/959579", "title"=>"The SOS network reconstructed using Lasso, TLasso, Algorithm 1 (List-SP) and CaSPIAN.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417088"], "description"=>"<p>The pseudocode corresponding to the CaSPIAN algorithm.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs"], "article_id"=>959590, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.t002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Algorithm_2_CaSPIAN_/959590", "title"=>"Algorithm 2: CaSPIAN.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417085"], "description"=>"<p>The pseudocode corresponding to the List-SP algorithm.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs"], "article_id"=>959587, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Algorithm_1_List_SP_/959587", "title"=>"Algorithm 1: List-SP.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417084"], "description"=>"<p>Solid arrows denote true-positives and dashed arrows denote false-positives. Precision is denoted by , sensitivity is denoted by , accuracy by , and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "hela", "inferred", "caspian"], "article_id"=>959586, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g006", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_HeLA_network_inferred_by_CaSPIAN_with_D_8202_8202_4_and_k_8202_8202_7_/959586", "title"=>"The HeLA network inferred by CaSPIAN with D = 4 and k = 7.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417096", "https://ndownloader.figshare.com/files/1417097"], "description"=>"<div><p>We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in <i>Saccharomyces cerevisiae</i>, and on data pertaining to the human <i>HeLa</i> cell network and the SOS network in <i>E. coli</i>. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime.</p></div>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "causal", "compressive", "sensing", "algorithm", "discovering", "directed", "interactions"], "article_id"=>959598, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0090781.s001", "https://dx.doi.org/10.1371/journal.pone.0090781.s002"], "stats"=>{"downloads"=>10, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CaSPIAN_A_Causal_Compressive_Sensing_Algorithm_for_Discovering_Directed_Interactions_in_Gene_Networks_/959598", "title"=>"CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417075"], "description"=>"<p>Solid arrows denote true-positives and dashed arrows denote false-positives. True-negatives and false-negatives are not depicted in the figures to avoid cluttering; however, they can be easily obtained by comparing the known regulatory network and the inferred network. Precision is denoted by , sensitivity by , accuracy by and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "hela", "reconstructed", "causal", "inference"], "article_id"=>959577, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g002", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_gene_regulatory_network_of_HeLa_cell_genes_reconstructed_using_different_causal_inference_algorithms_/959577", "title"=>"A gene regulatory network of HeLa cell genes, reconstructed using different causal inference algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417074"], "description"=>"<p>Solid arrows denote true-positives and dashed arrows denote false-positives. True-negatives and false-negatives are not depicted in the figures in order to avoid cluttering; however, they can be easily obtained by comparing the true regulatory network and the inferred networks. Precision is denoted by , sensitivity by , accuracy by , and the -measure by .</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "corresponding", "irma"], "article_id"=>959576, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.g001", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_gene_regulatory_network_corresponding_to_the_IRMA_network_obtained_using_different_algorithms_/959576", "title"=>"The gene regulatory network corresponding to the IRMA network, obtained using different algorithms.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-03-12 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1417087"], "description"=>"<p>The pseudocode corresponding to the CaSPIAN algorithm given a known subnetwork.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene regulation", "microarrays", "Regulatory networks", "genetics", "Gene networks", "Molecular cell biology", "gene expression", "algorithms", "Applied mathematics", "statistics", "Statistical methods", "Classical mechanics", "Time measurement", "communications", "Infographics", "graphs", "caspian"], "article_id"=>959589, "categories"=>["Physics", "Biological Sciences", "Mathematics", "Sociology"], "users"=>["Amin Emad", "Olgica Milenkovic"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0090781.t003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Algorithm_3_CaSPIAN_given_a_known_subnetwork_/959589", "title"=>"Algorithm 3: CaSPIAN given a known subnetwork.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-03-12 03:28:50"}

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