Statistical and computational model for high dimensional data analysis

高维数据分析的统计和计算模型

• 批准号: RGPIN-2016-06546
• 负责人: Wong, WilliamWaiLun
• 金额: $ 1.31万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676944
• 关键词: Statistical; computational; model; dimensional; data

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.**** **

在过去的十年中，对用于分析高维数据的统计和计算方法的研究得到了广泛的关注。在特征选择、分类和预测方面的方法学进展已广泛应用于许多领域，如药物发现过程、高通量基因组和遗传数据分析以及传染病建模。*******药物通常是一个小分子，与一些目标蛋白质的结合位点相互作用。药物发现过程通常可分为四个主要阶段：(i)目标识别；（ii）铅的发现；（三）临床试验；（四）监管审批和报销。药物发现是一个耗时和昂贵的过程。计算机辅助药物设计（CADD）是一门使用计算统计模型和算法来帮助药物发现过程的专业学科。*******我的NSERC DG研究计划的长期目标是开发有效和高效的统计和计算方法来分析CADD领域的各种高维数据。在未来5年，我的短期目标是在三个主要主题上取得进展：1)计算药物发现：通过多任务统计学习从药物信息学到药物经济学；2)遗传关联研究的统计方法；3)大规模人群传染病的个体水平建模。*******提出的研究为CADD提供了新的策略，并将对新药研发的规划阶段产生直接影响。利用药物经济学通过多任务统计学习影响药物发现方向，通过基于全基因组单倍型的关联分析检测基因-基因相互作用，以及使用近似贝叶斯计算方法改进基于agent的建模校准过程，将探索CADD研究领域的新领域。* * * * * *