Our research focuses on developing machine learning algorithms that will enable the use of an individual’s comprehensive biological information to predict or diagnose diseases, and to find or develop the best therapy for that individual.

It has recently become possible to retrieve molecular-level information from an individual, such as DNA sequence, gene expression levels in various tissues, epigenomic profile and other information. While such data is increasingly available, we are still unable to understand the genetic and molecular mechanisms that cause diseases. The challenge is due to the multifactorial nature of disease. The same disease can be caused by mutations in different genes or different pathogenic pathways. Unfortunately, current data analysis approaches fail to capture the complex relationship between disease and the vast amount of information in the molecular data.

The aim of our research is to resolve this challenge by developing machine learning algorithms that jointly model sophisticated interactions among many variables such as genetic variation, genes, pathways and disease, and robustly learn from vast amounts of data in order to better understand and treat disease. An approach that can robustly infer the pathways that can define disease processes will dramatically improve our understanding of diseases and advance personalized medicine in its treatment. We aim to realize this goal by using modern, advanced machine learning techniques.