Genetically and semantically aware homogeneous network for prediction and scoring of comorbidities.

Objective: Patients with comorbidities are highly prone to mortality risk than those suffering from a single disease. Therefore, quantification and prediction of disease comorbidities is necessary to stratify the mortality risk of the patients, predict the probability of their occurrence, design treatment strategies, and to prevent the progression of diseases. Enriching comorbidity disease relationships with rich semantics established by genetic components play a vital role in effectively quantifying and predicting comorbidities.

Enhanced disease-disease association with information enriched disease representation.

Objective: Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation.

Materials And Methods: An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation.