MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals
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{"title"=>"MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals", "type"=>"journal", "authors"=>[{"first_name"=>"Rosalba", "last_name"=>"Giugno", "scopus_author_id"=>"6506371678"}, {"first_name"=>"Alfredo", "last_name"=>"Pulvirenti", "scopus_author_id"=>"34977484800"}, {"first_name"=>"Luciano", "last_name"=>"Cascione", "scopus_author_id"=>"54580781600"}, {"first_name"=>"Giuseppe", "last_name"=>"Pigola", "scopus_author_id"=>"14058486800"}, {"first_name"=>"Alfredo", "last_name"=>"Ferro", "scopus_author_id"=>"57190518739"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369507789", "sgr"=>"84881127179", "issn"=>"19326203", "pmid"=>"23936357", "scopus"=>"2-s2.0-84881127179", "doi"=>"10.1371/journal.pone.0069873"}, "id"=>"7dfc9841-289f-3686-a189-19a94562dd4a", "abstract"=>"We present a new classification method for expression profiling data, called MIDClass (Microarray Interval Discriminant CLASSifier), based on association rules. It classifies expressions profiles exploiting the idea that the transcript expression intervals better discriminate subtypes in the same class. A wide experimental analysis shows the effectiveness of MIDClass compared to the most prominent classification approaches.", "link"=>"http://www.mendeley.com/research/midclass-microarray-data-classification-association-rules-gene-expression-intervals", "reader_count"=>17, "reader_count_by_academic_status"=>{"Researcher"=>2, "Student > Ph. D. Student"=>10, "Student > Master"=>3, "Student > Bachelor"=>2}, "reader_count_by_user_role"=>{"Researcher"=>2, "Student > Ph. D. Student"=>10, "Student > Master"=>3, "Student > Bachelor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>6, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1144665"], "description"=>"<p>Let denote the expression value of sample on the -th gene (an example of entry in M is shown as a black box). Samples are divided into classes corresponding to phenotypes disease. After discretization process, MIDClass constructs a matrix from by replacing each with the unique interval containing it. denotes an entry in (an example of entry in is shown as a black box). Then, MIDClass computes per class the possible sets of that are frequent and they have maximal size. MIDClass filters out gene expression intervals which size are below a given threshold. Since, association rules express interesting relationships between gene expressions and class labels, MIDClass uses them for classification. Therefore, MIDClass extracts a set of rules per class. Each rule has quantitative attributes on the antecedence part (i.e. discretized values) and one categorical attribute on the consequence side (i.e. the class ). Finally, it returns only rules that have a maximal score. The score takes into account the number of items in each sample are contained in the rule together with the cardinality of the rule (the computation of the score is described in detailed in the Methods section).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools", "midclass", "flowchart", "cancer", "dataset"], "article_id"=>765790, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873.g002", "stats"=>{"downloads"=>5, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Example_of_MIDClass_flowchart_on_Breast_Cancer_2_Dataset_data_are_partially_shown_/765790", "title"=>"Example of MIDClass flowchart on Breast Cancer 2 Dataset (data are partially shown).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-06 02:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1144674"], "description"=>"<p>Number of genes used by classifiers in each tested dataset.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools", "genes", "classifiers", "tested"], "article_id"=>765799, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873.t002", "stats"=>{"downloads"=>1, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_genes_used_by_classifiers_in_each_tested_dataset_/765799", "title"=>"Number of genes used by classifiers in each tested dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-06 02:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1144675"], "description"=>"<div><p>We present a new classification method for expression profiling data, called MIDClass (Microarray Interval Discriminant CLASSifier), based on association rules. It classifies expressions profiles exploiting the idea that the transcript expression intervals better discriminate subtypes in the same class. A wide experimental analysis shows the effectiveness of MIDClass compared to the most prominent classification approaches.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools", "microarray", "classification", "rules"], "article_id"=>765800, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MIDClass_Microarray_Data_Classification_by_Association_Rules_and_Gene_Expression_Intervals_/765800", "title"=>"MIDClass: Microarray Data Classification by Association Rules and Gene Expression Intervals", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-06 02:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1144673"], "description"=>"<p>We report the average accuracy of all tested classifiers on the selected dataset obtained with standard LOOCV. The performances concerning the compared algorithms have been retrieved from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069873#pone.0069873-Wang1\" target=\"_blank\">[17]</a>. Concerning MIDClass , in brackets we report the discretization algorithm, the MFI threshold and the function (1: 2: ).</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools", "midclass", "classifiers"], "article_id"=>765798, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873.t003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparisons_of_MIDClass_single_gene_classifiers_and_standard_classifiers_/765798", "title"=>"Comparisons of MIDClass , single gene classifiers and standard classifiers.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-08-06 02:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1144669"], "description"=>"<p>MIDClass ROC curves.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools", "roc"], "article_id"=>765794, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MIDClass_ROC_curves_/765794", "title"=>"MIDClass ROC curves.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-06 02:10:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/1144662"], "description"=>"<p>MIDClass flowchart.</p>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Biological data management", "microarrays", "systems biology", "algorithms", "software engineering", "Software tools"], "article_id"=>765788, "categories"=>["Information And Computing Sciences", "Engineering", "Biological Sciences"], "users"=>["Rosalba Giugno", "Alfredo Pulvirenti", "Luciano Cascione", "Giuseppe Pigola", "Alfredo Ferro"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0069873.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_MIDClass_flowchart_/765788", "title"=>"MIDClass flowchart.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-06 02:10:11"}
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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[284, 491, 620, 738, 843, 945, 1043, 1137, 1225, 1315, 1400, 1479, 1555]}, {"subject_area"=>"/Computer and information sciences/Artificial intelligence", "average_usage"=>[271, 413, 528, 633, 737, 815, 890, 970, 1042, 1119, 1222, 1287, 1315]}, {"subject_area"=>"/Engineering and technology/Management engineering", "average_usage"=>[221, 395, 464, 531, 614, 708, 792, 887, 945, 1051, 1131, 1209, 1251]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}, {"subject_area"=>"/Medicine and health sciences/Hematology", "average_usage"=>[238, 424, 545, 650, 749, 839, 926, 1006, 1090, 1167, 1243, 1316, 1380]}, {"subject_area"=>"/Medicine and health sciences/Oncology", "average_usage"=>[249, 468, 599, 718, 820, 920, 1008, 1093, 1181, 1281, 1357, 1444, 1517]}]}
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