LeMeDISCO: A computational method for large-scale prediction & molecular interpretation of disease comorbidity

Often different diseases tend to co-occur (i.e., they are comorbid), which yields the question: what is the molecular basis of their coincidence? Perhaps, common proteins are comorbid disease drivers. To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO ( L arge-Scal e M olecular Int e rpretation of Dis ease Co morbidity), an algorithm that predicts disease comorbidities from shared mode of action (MOA) proteins predicted by the AI-based MEDICASCY algorithm. LeMeDISCO was applied to predict the general occurrence of comorbid diseases for 3608 distinct diseases. To illustrate LeMeDISCO ’s power, we elucidate the possible etiology of coronary artery disease and ovarian cancer by determining the comorbidity enriched MOA proteins and pathways and suggest hypotheses for subsequent scientific investigation. The LeMeDISCO web server is available for academic users at: http://sites.gatech.edu/cssb/LeMeDISCO .