A New Web 2.0 Platform for Analyzing Genomics Data with Application to the S

描述（由申请人提供）：一个主要的研究重点是深入了解癌症的分子基础，以确定新的治疗方法为目标，是癌细胞的系统分析。它产生了大量的数据，包括化合物效价和基因表达数据。为了更有效地找到治疗方法，必须以对环境敏感的方式对数据进行整合和关联。该项目的总体目标是创建一个基于网络的高性能可视化分析平台，用于探索化合物活性和分子剖面数据，通过交互式可视化支持人类分析推理。该平台将展示如何对基于癌细胞的数据（包括基因组学、蛋白质组学、细胞毒性效力和其他研究数据）进行视觉探索，以确定癌症化疗的候选靶点和药物，并确定导致化疗耐药和敏感性的因素。具体目标如下：1)提高在高度协调的多视图可视化中导航、搜索和直接探索大数据的能力。热图将结合缩放界面和动画的交互机制。一种新的可视化Tau路径统计方法的结果将探索成对的变量，如化合物-基因，其中在一些细胞系中发现了显著的关联，但不是所有的细胞系。2)为多核系统开发高性能分析服务器。服务器将执行并发组件进行显著性测试、聚类、相关性、回归和关联分析。3)扩展新的Tau路径统计方法。扩展包括改进的亚群识别，条件测试，以确定亚群下的关联显著大于整个群体，以及扩展到更大的人群，如所有感兴趣的化合物，以便优先考虑候选药物。4)整合资源与平台，使发现能够快速验证并提供额外的见解。这些资源包括生物途径系统（如KEGG）、基因本体注释（GO）、生物筛选数据库（如PubChem）和化学信息学服务（如OpenTox）。由此产生的可视化分析平台的影响将是更快地识别导致发现新的癌症治疗方法的假设。这些假设包括可能在寻找新化疗药物的药物发现项目中作为先导的化合物，以及在选定的细胞系中调节药物效力的基因。该项目将为癌症研究和策划公共内容提供一个可视化分析平台。

DESCRIPTION (provided by applicant): A major research focus for gaining insight into the molecular basis for cancer, with the goal of identifying new treatments, is the systematic profiling of cancer cells. It has resulted in large volumes of data that include the compound potency and gene expression data. To more effectively find treatments, the data must be integrated and associated in context sensitive ways. The overall objective of this project is to create a web-based, high-performance visual analytics platform for the exploration of compound activity and molecular profile data, through interactive visualization that supports human analytical reasoning. The platform will demonstrate how visual exploration of cancer cell-based data that include genomics, proteomics, cytotoxic potency, and other research data can be used to identify candidate targets and drugs for cancer chemotherapy, and determine factors that contribute to chemoresistance and sensitivity. The specific aims are the following: 1) Improve the ability to navigate, search, and directly explore large data in highly-coordinated multi-view visualizations. The heatmap will incorporate interaction mechanisms from zooming interfaces and animation. A new visualization for results of the Tau Path statistical method will explore pairs of variables, such as compound-gene, where significant associations have been found in some, but not all of the cell lines. 2) Develop a high-performance analytics server for multicore systems. The server will execute concurrent components for significance tests, clustering, correlation, regression, and association analysis. 3) Extend the new Tau Path statistical method. The extensions include improved identification of the subpopulation, conditional testing to identify subpopulations under which associations are significantly greater than for the whole population, and extension to larger populations, such as all compounds of interest, so as to prioritize drug candidates 4) Integrate resources with the platform to enable findings to be rapidly validated and provide additional insights. The resources include biological pathways systems (e.g. KEGG), the Gene Ontology annotations (GO), biological screening databases (e.g. PubChem), and chemoinformatics services (e.g. OpenTox). The impact of the resulting visual analytics platform will be to more rapidly identify hypotheses that lead to the discovery of new cancer treatments. These hypotheses include compounds that may serve as leads in a drug discovery program seeking new chemotherapy and genes that modulate drug potencies in selected cell lines. The project will lead to a visual analytics platform for cancer research and curated public content.