Using text analysis for chronic disease management
The diagnosis, management, and treatment of chronic diseases (e.g., diabetes, chronic obstructive pulmonary diseases, and heart failure) have traditionally been focused on longitudinal histories and physical examinations as primary tools of assessment, and augmented by laboratory testing and imaging. Equally important to history taking and physical examinations is the objective assessments and understanding of the contribution of the patients' states of mind to their disease states. This is historically only documented qualitatively but highly challenging to measure quantitatively.
Application of deep learning approaches in modelling cheminformatics data and discovery of novel therapeutic agents for prostate cancer
The recent explosion of chemical and biological information calls for fundamentally novel ways of dealing with big data in the life sciences. This problem can potentially be addressed by the latest technological breakthroughs on both software and hardware frontiers. In particular, the latest advances in artificial intelligence (AI) enable cognitive data processing at very large-scale by means of deep learning (DL).
Modeling multiple types of "omics" data to understand the biology of human exposure to pollution and allergens
Inhaled environmental and occupational exposures such as air pollution and allergens are known to have a profound effect on our respiratory and immunological health. This collaborative project seeks to better understand how the human body responds adversely to these perturbants by developing and applying new computational models for analyses of integrated molecular data sets, collectively known as 'omics profiling (e.g., genomics, proteomics, metabolomics, epigenomics, transcriptomics, and polymorphisms).