Statistical and computational model for high dimensional data analysis

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Research towards the development of statistical and computational methodologies for analyzing high dimensional data has gained much attention over the last decade. Methodological advances with respect to feature selection, classification, and predication have been widely applied in many fields, such as the drug discovery process, high throughput genomic and genetic data analysis, and infectious disease modeling. A drug is often a small molecule that interacts with the binding sites of some target proteins. The drug discovery process can typically be divided into four main stages: (i) target identification; (ii) lead discovery; (iii) clinical trials; and (iv) regulatory approval and reimbursement. Drug discovery is a time consuming and costly process. Computer-Aided Drug Design (CADD) is a specialized discipline that uses computational statistics models and algorithms to aid the drug discovery process. The long-term objective of my NSERC DG research program is to develop effective and efficient statistical and computational methods for analyzing diverse types of high dimensional data in the CADD areas. In the next 5 years, my short-term objectives are to make advances in three main themes: 1) computational drug discovery: from pharmacoinformatics to pharmacoeconomics via multi-task statistical learning; 2) statistical methods for genetic association studies; and 3) individual-level modeling of infectious diseases in large populations. The proposed research provides new strategies for CADD and will have direct impact on the planning stage of the research and development of new drugs. New territory in the research area of CADD will be explored by using pharmacoeconomics to influence the direction of drug discovery via multi-task statistical learning, detecting gene-gene interactions via genome-wide haplotype-based association analysis, and improving agent-based modeling calibration process by using the approximate Bayesian computation method.