Postdoctoral Research Fellowship in Interdisciplinary Informatics for FY 2003

跨学科信息学博士后研究奖学金由数学和物理科学理事会(MPS)和生物科学理事会(BIO)联合赞助，以鼓励跨越它们之间传统学科界限的研究和培训。这些奖学金为寻求使用信息学工具和方法对生物学问题进行研究的众多新近获得博士学位的人(生物学家、化学家、物理学家、数学家、统计学家、计算机科学家等)提供了在生物学和信息学方面进行跨学科研究和教育活动的机会。预计通过这些研究金培训的研究员将在培训未来的工作人员方面发挥重要作用。信息学博士后培训将允许受过生物、数学、化学和物理科学培训的初级科学家在开发新的量化工具和方法方面发挥关键作用，这些工具和方法将促进生物学和其他领域的信息学。该奖学金的研究和培训计划名为“基因表达微阵列数据的转化”。基因表达微阵列数据的统计分析可以通过使用数据转换大大简化，这使数据更接近于标准统计技术的共同假设。这项研究开发了使用最大似然法和稳健的统计技术为微阵列数据找到适当转换的方法。

Postdoctoral Research Fellowships in Interdisciplinary Informatics are sponsored jointly by the Directorates for Mathematical and Physical Sciences (MPS) and Biological Sciences (BIO) to encourage research and training that cross the traditional disciplinary boundaries between them. These fellowships provide opportunities for interdisciplinary research and educational activities in biology and informatics to a wide range of recent doctoral recipients (biologists, chemists, physicists, mathematicians, statisticians, computer scientists, and others) who seek to conduct research on biological questions using informatics tools and methods. It is expected that the Fellows trained through these fellowships will play an important role in training the future workforce. Postdoctoral training in informatics will permit junior scientists trained in biology, mathematical, chemical, and physical sciences to play key roles in developing new quantitative tools and methods that will advance informatics in biology and other fields. The research and training plan for this fellowship is entitled "Transformations for gene-expression microarray data." Statistical analysis of gene-expression microarray data can be greatly simplified by use of data transformations which bring the data more closely in line with the assumptions common to standard statistical techniques. This research develops methods for finding appropriate transformations for microarray data, using maximum-likelihood methods and robust statistical techniques.