Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression.

ABSTRACT: Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally,

SUBMITTER: Marx P 

PROVIDER: S-EPMC5507322 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature