Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases

PLoS Computational Biology

Volumn 13, Issue 10, 2017, Pages

  Mezlini, Aziz M ^a,b   Goldenberg, Anna ^a,b  

^a UNIVERSITY OF TORONTO   (Canada)

^b HOSPITAL FOR SICK CHILDREN   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION REACTIONS; BAYESIAN NETWORKS; COMPLEX NETWORKS; GRAPHIC METHODS; PROTEINS;

BAYESIAN FRAMEWORKS; COMPLEX DISEASE; GENE DISCOVERY; GENE-DISEASE ASSOCIATIONS; GENETIC MECHANISM; HARD PROBLEMS; HUMAN DISEASE; POWER; PROBABILISTIC GRAPHICAL MODELS; PROTEIN-PROTEIN INTERACTION NETWORKS;

GENES;

ARTICLE; BAYES THEOREM; BIOLOGY; CONCEPTUAL FRAMEWORK; DIFFUSION; DISEASE ASSOCIATION; DISEASES; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; GENE; GENETIC ASSOCIATION; GENETIC SCREENING; HUMAN; MATHEMATICAL MODEL; PHENOTYPE; PROBABILISTIC GRAPHICAL MODEL; WHOLE EXOME SEQUENCING; ALGORITHM; AUTISM; EPILEPSY; GENE EXPRESSION PROFILING; GENETICS; METABOLISM; NEOPLASM; PROCEDURES; PROTEIN ANALYSIS; SCHIZOPHRENIA; STATISTICAL MODEL;

ALGORITHMS; AUTISTIC DISORDER; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DISEASE; EPILEPSY; GENE EXPRESSION PROFILING; HUMANS; MODELS, STATISTICAL; NEOPLASMS; PROTEIN INTERACTION MAPPING; SCHIZOPHRENIA;

EID: 85031896131     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005580     Document Type: Article

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