Advancing patient treatment through integrative computational biology

Abstract

Integrative computational biology and artificial intelligence help improving treatment of complex diseases by building explainable models. From systematic data analysis to improved biomarkers, drug mechanism of action, and patient selection, such analyses influence multiple steps of drug discovery pipeline. Data mining, machine learning, graph theory and advanced visualization help characterize interactome and drug orphans with accurate predictions, making disease modelling more comprehensive. Intertwining computational prediction and modelling with biological experiments will lead to more useful findings faster and more economically.