Statistical methods for genomic research

生物学家和临床科学家越来越多地产生高通量的分子数据，希望阐明和了解疾病的机制、诊断和预后。特别是，DNA微阵列是研究基因表达及其与临床结果的可能相关性的重要技术。然而，这些由现代和快速发展的技术产生的高维数据伴随着几个分析挑战，需要有效的统计和计算方法。我的研究将增加新的和增强的统计和计算工具，用于高通量分子和临床数据的分析和集成。

Biologists and clinical scientists are increasingly generating high-throughput molecular data with the hope of elucidating and understanding disease mechanisms, diagnosis and prognosis. In particular, DNA microarrays are an important technologies for studying gene expression and the possible correlations with clinical outcomes. However, these high-dimensional data generated by modern and rapidly evolving technologies come with several analytical challenges that require effcient statistical and computational methodologies. My research will add new and enhanced statistical and computational tools for the analysis and integration of high-throughput molecular as well as clinical data.