Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases
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Mendeley | Further Information

{"title"=>"Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases", "type"=>"journal", "authors"=>[{"first_name"=>"Aziz M.", "last_name"=>"Mezlini", "scopus_author_id"=>"55325502100"}, {"first_name"=>"Anna", "last_name"=>"Goldenberg", "scopus_author_id"=>"23392374000"}], "year"=>2017, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-85031896131", "sgr"=>"85031896131", "doi"=>"10.1371/journal.pcbi.1005580", "pui"=>"619081544", "pmid"=>"29023450", "isbn"=>"1111111111", "issn"=>"15537358"}, "id"=>"afeccb99-68b9-36d2-a5a8-a38aedea2d7e", "abstract"=>"Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using net-works within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection com-pared to individual gene testing. We then show consistently improved performance of Con-flux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experi-ments show that our novel Bayesian framework Conflux incorporates many of the advan-tages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios. Author summary", "link"=>"http://www.mendeley.com/research/incorporating-networks-probabilistic-graphical-model-find-drivers-complex-human-diseases", "reader_count"=>60, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>24, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>8, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Researcher"=>13, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>24, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>8, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>16, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>7, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Engineering"=>4, "Biochemistry, Genetics and Molecular Biology"=>17, "Medicine and Dentistry"=>1, "Neuroscience"=>4, "Physics and Astronomy"=>1, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>16}, "Computer Science"=>{"Computer Science"=>7}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>17}}, "group_count"=>6}

Scopus | Further Information

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