A Data Driven Approach to Diagnosing and Treating Disease

Throughout the biomedical and life sciences research community, advanced integrative biology algorithms are employed to integrate large scale data across many different high-dimensional datatypes to construct predictive network models of disease. The causal inference approaches we employ for this purpose well complement the types of natural artificial intelligence/machine learning approaches that have become nearly standard in the life and biomedical sciences for building classifiers for a range of problems, from disease classification and subtype stratification, to the identification of responders and non-responders for a given treatment strategy. By building a causal network model that spans multiple scales (from the molecular to the cellular, to the tissue/organ, to the organism and community) we can understand the flow of information and how best to modulate that flow to improve human wellbeing, whether better diagnosing and treating disease or improving overall health(1-4). More specifically, we have constructed predictive network models for Alzheimer's disease, along with other common human diseases such as obesity, diabetes, heart disease, and inflammatory bowel disease, and cancer, and demonstrated a causal network common across all of these diseases(3, 5-10). Not only do we demonstrate that our predictive models uncover important mechanisms of disease and mechanistic connections among different diseases, but that they have led to a natural way to prioritize therapeutic points of intervention and provide optimal molecular phenotypes for high throughput screening. Our application of these models in a number of disease areas has led to the identification of novel genes that are causal for disease and that may serve as efficacious points of therapeutic intervention, as well as to personalized treatment strategies that provide a more quantitative and accurate approach to tailoring treatments to specific forms of disease.