Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks
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{"title"=>"Automatic context-specific subnetwork discovery from large interaction networks", "type"=>"journal", "authors"=>[{"first_name"=>"Ashis", "last_name"=>"Saha", "scopus_author_id"=>"56041773800"}, {"first_name"=>"Aik Choon", "last_name"=>"Tan", "scopus_author_id"=>"8949996200"}, {"first_name"=>"Jaewoo", "last_name"=>"Kang", "scopus_author_id"=>"8914056400"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84894240196", "doi"=>"10.1371/journal.pone.0084227", "issn"=>"19326203", "pui"=>"372426336", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"24392115", "scopus"=>"2-s2.0-84894240196"}, "id"=>"5cdc989b-a34a-3313-9e0e-cf70e0292fb6", "abstract"=>"Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.", "link"=>"http://www.mendeley.com/research/automatic-contextspecific-subnetwork-discovery-large-interaction-networks", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>7, "Student > Master"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>7, "Student > Master"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>9, "Medicine and Dentistry"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>9}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Norway"=>1, "Brazil"=>1, "Slovenia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1336302"], "description"=>"<p>Communities are extracted from the molecular interaction network to generate Molecular Interaction Subnetworks (MISs)(A–B). Each MIS is mapped to microarray probes and all the samples are clustered according to the expression pattern of a certain number of highly differentially expressed probes (3 probes in this example figure). MISs are ranked by the entropy score which is lowest when every cluster contains only one type (phenotype) of samples (C–D). Finally, the top MISs cast votes to predict the context (phenotype) of a new sample. The voting depends on the proportion of different types of samples in the cluster closest to the new sample (E–G).</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms"], "article_id"=>892358, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_COSSY_/892358", "title"=>"Overview of COSSY.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336303"], "description"=>"<p>The top rectangle marked by C1 shows that REDPTC and TRK have an indirect effect on the activation of Ras. Ras then activates BRAF, and BRAF phosphorylates MEK which in turn phosphorylates ERK. The result of this path is proliferation survival. This pathway has five connected components (C1–C5). Among them, C2 is actually a subset of C1, and the others are fully disconnected, i.e., there is no significant interaction between any pair of the components.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "thyroid", "cancer", "pathway", "kegg"], "article_id"=>892359, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_thyroid_cancer_pathway_in_KEGG_ID_hsa05216_/892359", "title"=>"The thyroid cancer pathway in KEGG (ID: hsa05216).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336304"], "description"=>"<p>Let three probes (i, j, and k) constitute the representative probeset of an MIS. We plot all the samples with the expression values of these probes in separate dimensions, and then we cluster the samples. If the samples in a cluster are mostly of one kind (such as or ), we can say that the cluster’s expression pattern represents the corresponding class (positive or negative). The ranking of an MIS producing such clusters should be high.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms"], "article_id"=>892360, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ranking_of_MIS_/892360", "title"=>"Ranking of MIS.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336305"], "description"=>"<p>A–C) Three overlapped subnetworks from three different pathways constitute the MIS. D) The merged MIS is shown here. E) The expression heatmap of the representative probeset of the MIS is shown here.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "ranked", "kegg", "mis", "leukemia"], "article_id"=>892361, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_top_ranked_KEGG_MIS_in_the_Leukemia_dataset_/892361", "title"=>"The top ranked KEGG MIS in the Leukemia dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336306"], "description"=>"<p>COSSY [KEGG] and COSSY [STRING] stand for COSSY using KEGG and STRING, respectively. <i>k</i>-TSP and DIRAC are the classification algorithms described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Tan1\" target=\"_blank\">[5]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Eddy1\" target=\"_blank\">[17]</a>, respectively. SVM stands for the Support Vector Machine algorithm.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "notable"], "article_id"=>892362, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LOOCV_accuracy_of_five_notable_classifiers_/892362", "title"=>"LOOCV accuracy of five notable classifiers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336307"], "description"=>"<p>The number at the end of the name of an MIS indicates its rank.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "leukemia"], "article_id"=>892363, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stitching_of_the_MISs_found_from_the_Leukemia_dataset_/892363", "title"=>"Stitching of the MISs found from the Leukemia dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336308"], "description"=>"<p>AUC has been calculated using the ROCR package in R <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Sing1\" target=\"_blank\">[35]</a>. The best AUC for each dataset is highlighted in bold face.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "cossy", "dirac"], "article_id"=>892364, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Area_Under_Curve_AUC_values_of_COSSY_and_DIRAC_for_different_datasets_/892364", "title"=>"Area Under Curve (AUC) values of COSSY and DIRAC for different datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336309"], "description"=>"<p>The leftmost column contains the names of the methods; the rightmost column shows the average accuracy of each method for seven datasets, and other columns show the accuracy (%) for individual datasets. ‘COSSY [KEGG]’ and ‘COSSY [STRING]’ represent COSSY using KEGG and STRING, respectively. ‘DIRAC’ is the algorithm proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Eddy1\" target=\"_blank\">[17]</a> whose LOOCV accuracies have been calculated using the matlab code published with the paper. <i>k</i>-TSP and TSP denote the classification algorithms described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Tan1\" target=\"_blank\">[5]</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Geman1\" target=\"_blank\">[4]</a>, respectively. SVM stands for Support Vector Machine. ‘Doublet [Sign-DT]’, ‘Doublet [Sumdiff-DT]’, and ‘Doublet [Mul-DT]’ denote the classification methods using Sign-Doublet, Sumdiff-Doublet, and Mul-Doublet, respectively, with decision trees as described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Chopra1\" target=\"_blank\">[6]</a>. The last three rows contain the loocv accuracies using Nave Bayes, <i>k</i> Nearest Neighbor, and PAM classifier, respectively.</p><p>Results obtained from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Tan1\" target=\"_blank\">[5]</a>.</p><p>Results obtained from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084227#pone.0084227-Chopra1\" target=\"_blank\">[6]</a>.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms"], "article_id"=>892365, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_LOOCV_accuracy_of_classifiers_/892365", "title"=>"LOOCV accuracy (%) of classifiers.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336310"], "description"=>"<p>The first column, ‘Dataset Name’, indicates the name of the microarray dataset used in the manuscript. ‘#Probes’ shows the number of probes present in the dataset. The third and fourth columns contain the name of the positive and negative class, respectively, followed by the number of samples of that class. The last column shows the reference the dataset was collected from.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms"], "article_id"=>892366, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Microarray_Datasets_/892366", "title"=>"Microarray Datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336311"], "description"=>"<p>The table shows the number of MISs with a total of nodes below, within, and above the appropriate range.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "subnetwork"], "article_id"=>892367, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Molecular_Interaction_Subnetwork_Size_/892367", "title"=>"Molecular Interaction Subnetwork Size.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336312"], "description"=>"<p>The ‘Total Nodes’ column contains the total number of nodes available in the network while the ‘Gene (Protein) Nodes’ column shows the number of nodes with at least one gene in KEGG (or one protein in STIRING). The fourth and fifth columns contain the total number of edges, and the number of connected components having at least one gene (or protein), respectively. ‘Avg. Node Degree’ represents the number of edges a node has on average. ‘Max Node Degree’ denotes the maximum number of edges a node has in the network. ‘Clustering Coefficient’ is the ratio of the triangles to the connected triples in a graph.</p>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "kegg"], "article_id"=>892368, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_Properties_of_KEGG_and_STRING_/892368", "title"=>"Network Properties of KEGG and STRING.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1336313"], "description"=>"<div><p>Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.</p></div>", "links"=>[], "tags"=>["Computational biology", "Molecular genetics", "Gene classes", "Gene identification and analysis", "gene expression", "Metabolic networks", "microarrays", "Regulatory networks", "Signaling networks", "genetics", "Human genetics", "Genetic association studies", "Gene networks", "Genetics of disease", "algorithms", "context-specific", "subnetwork"], "article_id"=>892369, "categories"=>["Information And Computing Sciences", "Biological Sciences"], "users"=>["Ashis Saha", "Aik Choon Tan", "Jaewoo Kang"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0084227"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automatic_Context_Specific_Subnetwork_Discovery_from_Large_Interaction_Networks_/892369", "title"=>"Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-01 02:43:26"}

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Relative Metric

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