Data Science Core

数据科学核心摘要 德克萨斯A&M超级基金研究中心的目标是探索和开发描述性模型， 可以预测环境期间化学品暴露可能产生的危险后果的工具 我们的目标是，在紧急情况下采取有效措施，并制定有力的解决办法，减轻对人类健康的负面影响。的 该中心的最终目标是提高规划和控制的决策能力， 突发环境污染事件。数据科学核心是 该中心将通过支持四个具有挑战性的工作，为实现中心的目标作出贡献。 研究项目。这些项目将产生需要全面分析的高维数据， 在国家的最先进的数据科学方法的专业知识，以便将原始实验数据转化为 可操作的见解和预测模型。导演：Dr. Christodoulos A. Floudas与 共同研究员Fred A.赖特，数据科学核心将提供多种方法和服务， 中心研究人员在三个具体目标：（一）通过分享专业知识和提供支持，通过先进的 数据科学和统计方法;（ii）通过开发高性能，新颖的方法， 同时回归或分类与降维和数据集成;以及（iii）通过 建设和维护一个计算平台，使整个中心的合作， 促进向更广泛的社区和主要利益相关者传播知识。研究项目1将 描述易受移动和再移动影响的受污染沉积物的暴露途径， 由于风暴活动造成的沉积;数据科学核心将为实验设计提供服务， 污染沉积物结合实验的假设检验和回归。项目2将研究 通过广泛作用的吸附材料减轻化学品对健康的不利影响;数据科学 岩心将通过高级回归和同步分析， 用非线性核降维来指导实验设计和材料性能 识别.项目3将研究组织间和个体间的变异性， 环境混合物;数据科学核心将应用复合分类和聚类策略， 化学混合物的特性。项目4将开发单细胞、高通量平台， 环境污染物和混合物的内分泌干扰物潜力;数据科学核心将有助于 在通过模型构建和减少预测性内分泌受体的活性中， 模型此外，数据科学核心将通过建立一个 通过计算平台服务实现数据共享和协作的理想环境。该平台将 还传播该中心的成果，包括获得最终的高性能预测模型 通过提供适合科学界使用的互动界面，

Data Science Core ABSTRACT The objective of the Texas A&M Superfund Research Center is to explore and develop descriptive models and tools that can predict the possible hazardous outcomes of chemical exposure during environmental emergencies and to produce powerful solutions which can mitigate the negative effects on human health. The ultimate goal of the Center is to contribute to decision-making capabilities for planning and control in emergency environmental contamination events. The Data Science Core is one of the essential components of the Center that will contribute to achieving the goals of the Center by supporting the work of four challenging Research Projects. The projects will produce high-dimensional data that requires comprehensive analysis and expertise in state-of-the-art data science methodologies in order to translate raw experimental data into actionable insights and predictive models. Directed by Dr. Christodoulos A. Floudas and in collaboration with Co-investigator Dr. Fred A. Wright, the Data Science Core will provide numerous methods and services to the Center researchers under three specific aims: (i) by sharing expertise and providing support via advanced methodologies in data science and statistics; (ii) by developing high-performance, novel methods for simultaneous regression or classification with dimensionality reduction and data integration; and (iii) by constructing and maintaining a computational platform that will enable collaboration across the Center and facilitate dissemination of knowledge to the wider community and key stakeholders. Research Project 1 will characterize exposure pathways of contaminated sediments that are vulnerable to movement and re- deposition due to storm activity; the Data Science Core will provide services for experimental design, hypothesis testing, and regression for contaminated sediment binding experiments. Project 2 will study the mitigation of adverse health effects of chemicals through broad-acting sorption materials; the Data Science Core will utilize predictive modeling of sorption activity via advanced regression and simultaneous dimensionality reduction with nonlinear kernels to guide experimental design and material property identification. Project 3 will investigate the inter-tissue and inter-individual variability in response to complex environmental mixtures; the Data Science Core will apply composite classification and clustering strategies for characterization of chemical mixtures. Project 4 will develop single-cell, high-throughput platforms to quantify the endocrine disruptor potential of environmental contaminants and mixtures; the Data Science Core will aid in predicting the activity of multiple endocrine receptors through model construction and reduction of predictive models. Furthermore, the Data Science Core will maximize productivity within the Center by establishing an ideal environment for data sharing and collaboration via a computational platform service. The platform will also disseminate the results of the Center, including access to the final high-performance predictive models and tools, by providing interactive interfaces amenable for use by the scientific community.