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{"title"=>"Using Information Interaction to Discover Epistatic Effects in Complex Diseases", "type"=>"journal", "authors"=>[{"first_name"=>"Orlando", "last_name"=>"Anunciação", "scopus_author_id"=>"37121440000"}, {"first_name"=>"Susana", "last_name"=>"Vinga", "scopus_author_id"=>"55893670600"}, {"first_name"=>"Arlindo L.", "last_name"=>"Oliveira", "scopus_author_id"=>"7201929537"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"24194833", "sgr"=>"84886052456", "doi"=>"10.1371/journal.pone.0076300", "scopus"=>"2-s2.0-84886052456", "pui"=>"370091171", "issn"=>"19326203"}, "id"=>"b1fc847f-1903-3410-bcc3-64500a6a74b1", "abstract"=>"It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerful to detect pairwise epistatic interactions than its competitors. We show results of the application of information interaction method to the WTCCC breast cancer dataset. Our results are validated using permutation tests. We were able to find 89 statistically significant pairwise interactions with a p-value lower than 10(-3). Even though many recent algorithms have been designed to find epistasis with low marginals, we observed that all (except one) of the SNPs involved in statistically significant interactions have moderate or high marginals. We also report that the interactions found in this work were not present in gene-gene interaction network STRING.", "link"=>"http://www.mendeley.com/research/using-information-interaction-discover-epistatic-effects-complex-diseases-3", "reader_count"=>13, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>4, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>4, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>4, "Neuroscience"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}}, "reader_count_by_country"=>{"Portugal"=>1, "Germany"=>1}, "group_count"=>1}

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• http://doi.org/10.1016/j.procs.2015.05.191
• http://doi.org/10.1016/j.engappai.2016.09.007
• http://doi.org/10.1186/s42400-019-0025-z

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Figshare

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PMC Usage Stats | Further Information

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• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[295, 511, 651, 775, 882, 992, 1100, 1201, 1304, 1400, 1486, 1570, 1650]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"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", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}, {"subject_area"=>"/Medicine and health sciences/Oncology", "average_usage"=>[249, 468, 599, 718, 820, 920, 1008, 1093, 1181, 1281, 1357, 1444, 1517]}, {"subject_area"=>"/Physical sciences", "average_usage"=>[254, 431, 547, 651, 748, 842, 932, 1017, 1098, 1178, 1259, 1336, 1404]}, {"subject_area"=>"/Physical sciences/Mathematics", "average_usage"=>[259, 431, 541, 639, 727, 816, 898, 980, 1061, 1136, 1214, 1294, 1356]}]}

F1000Prime