MEETING: Interactions between Omics and Statistics: Analyzing High Dimensional Data to be held at the 8th Intl Purdue Symposium on Statistics June 20-24, 2012, West Lafayette, IN

为了应对在许多领域产生的越来越多的高通量数据带来的挑战，特别是在植物基因组研究领域，将于2012年6月20-24日举行为期一天的题为“OMICS与统计之间的相互作用：分析高维数据”的会议，作为第八届国际普渡统计研讨会的一部分。整个专题讨论会的主题是“21世纪统计科学的多样性”，会议将作为植物生物学和统计学领域的知名研究人员的互动前沿组织，以弥合植物生物学家和统计学家之间的差距，并为解决与高维数据分析有关的问题提供新的见解。演讲的主题包括但不限于，对高维组学数据的总体介绍，这些数据的特殊统计挑战，以及分析这些高维数据的新开发的统计方法。此外，还将讨论进一步完善此类高维组学数据分析的未来方向。在专题讨论会上将举办一个题为“iPLANT数据存储和iPLANT发现环境”的配套研讨会，该研讨会将由iPLANT协作组组织和介绍。NSF基金将部分支付研究生和博士后参加研讨会、会议和研讨会的费用，作为他们在高度跨学科的统计基因组学领域培训的一部分。

To meet the challenges brought by the increasingly amount of high-throughput data generated in many fields, especially in the area of plant genome research, a one-day session entitled "Interactions Between Omics and Statistics: Analyzing High Dimensional Data" will be held as part of the 8th International Purdue Symposium on Statistics June 20 - 24, 2012. The theme of the overall symposium is "Diversity in the Statistical Sciences for the 21st Century" and the session will be organized as an interactive frontier for prestigious researchers within plant biology and statistics, to bridge the gap between plant biologists and statisticians, and provide new insight into addressing the issues associated with high-dimensional data analysis. The topics of presentations include, but are not limited to, an overall introduction of high dimensional omics data, the special statistical challenges of these data, and the newly developed statistical methods to analyze these high dimensional data. In addition, future directions will be discussed to further improve the analysis of such high dimensional omics data. A companion workshop featured in the symposium entitled "iPlant Data Store and iPlant Discovery Environment" will be organized and presented by the iPlant Collaborative. NSF funds will in part defray the costs of participation of graduate students and postdocs to attend the symposium, session and workshop as part of their training in the highly interdisciplinary field of statistical genomics.