S-Space College of Medicine/School of Medicine (의과대학/대학원) Dept. of Medicine (의학과) Journal Papers (저널논문_의학과)
The translational network for metabolic disease – from protein interaction to disease co-occurrence
- Issue Date
- BMC Bioinformatics, 20(1):576
- Semi-supervised learning ; Disease network ; Comorbidity ; Protein interaction ; Disease scoring
The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing n-of-1 utility (n potential diseases of one patient) to human disease network—the translational disease network.
We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network.
The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.