Models and Computational Strategies in Statistical Bioinformatics

这位研究人员和他的同事将开发高通量生物数据的方法，这些方法提供现代统计构建块的创新扩展，包括使用随机效应进行正则化、收缩估计、贝叶斯统计，以及使用混合方法进行后验分类和预测。对期望最大化算法提出了一种新的改进方法，用于在几个重要的生物学应用中进行可扩展和高效的模型拟合和推理。该项目的目标是开发高通量生物数据估计的新的统计模型、计算算法和决策理论分析。这些新方法可以应用于微阵列分析、RNA测序计数、无标记鸟枪蛋白质组学、代谢组学、数量性状基因座的识别和关联作图。

The investigator and his colleagues will develop methods for high-throughput biological data that provide innovative extensions of modern statistical building blocks, including the use of random effects for regularization, shrinkage estimation, Bayesian statistics, and mixtures for posterior classification and prediction. Novel modifications of the expectation-maximization algorithm are proposed for scalable and efficient model fitting and inference in several important biological applications.The goal of this project is to develop new statistical models, computational algorithms, and decision theoretic analysis of estimates for high-throughput biological data.  The new methods can be applied in the analysis of microarrays, RNA sequencing counts, label-free shotgun proteomics, metabolomics, the identification of quantitative trait loci and association mapping.