Training in Biomedical Discovery from Large Scale Data Sets

基因组学、蛋白质组学和先进成像技术的发展导致了 大量生物学数据的积累。随着大规模数据集在 生物医学研究，我们正在接近一个范式转变，其中发现的过程是数据驱动的， 数据既是假设的来源，也是检验假设的手段。这些海量的数据 是丰富的信息来源;然而，提取有意义的信息可能是一个艰巨的挑战， 常常成为发现过程的瓶颈。因此，迫切需要跨学科 培训了解数据、数据如何产生以及数据用途的科学家。在 此外，这些科学家必须成为新计算工具的开发人员和高技能用户 分析大型数据集的必要性。该计划的目标是培养学生成为精通 以下方面：1.数据采集。这将包括基因组学，蛋白质组学和 显像2.计算。这将包括数学和统计算法的知识， 实施有效的计算机代码以及强调数据仓库的方法， 关系、演绎和其他数据库。3.数据整合。这是一个关键领域， 从各种空间和时间尺度的异质数据集中获得有用的信息。它将包括 从分子到生物体水平的系统建模和模拟方法的知识。 还将强调计算数据挖掘方法。该计划的核心将是 在至少两名来自不同领域的导师的指导下，在跨学科团队中进行基于研究的培训 学科（即，计算/数学和生物医学科学）。培训活动将包括 专门的教学课程以及研讨会，期刊俱乐部和师生务虚会。这将是 一个跨机构的培训计划，教师来自计算机科学， 在墨西哥湾沿岸财团的五个参与机构中， 休斯顿地区。培训有能力管理和从大型数据集中提取信息的科学家将 极大地促进了传染病和癌症等领域的生物学发现， 培训计划将对公众健康产生直接的积极影响。

The development of genomics, proteomics and advancedimaging technology has resulted in the accumulation of vast amounts of biological data. As large scale data sets become predominant in biomedical research, we are approaching a paradigm shift in which the process of discovery isdata-driven, and in which data are the source of hypotheses as well as the means for testing them. These masses ofdata are rich sources of information; however, extracting meaningful information can be a daunting challenge, and often presents a bottleneck for the discovery process. Thus, there is a pressing need for interdisciplinary training of scientists who understand the data, how they are generated, and what they are used for. In addition, these scientists must become developers and highly skilled users of the new computational tools necessary to analyze large data sets. The goal of this program is to train students to become proficient in the following areas: 1. Data acquisition. This will include knowledge of the methods of genomics, proteomicsand imaging. 2. Computation. This will include knowledge of mathematical and statistical algorithms, implementation of effective computer codes as well as an emphasis on methods of data warehousing in relational, deductive and other databases. 3. Data integration. This is a critical area that involves extracting useful information from the heterogeneous data sets at various spatial and temporal scales. It will include knowledge of methods of modeling and simulation of systemsfrom the molecular to the organismal level. There will also be an emphasis on computational data mining methods. The core of the program will be research-based training in interdisciplinary teams under the guidance of at least two mentors from disparate disciplines (i.e., computational/mathematical and biomedical sciences). Training activities will consist of specialized didactic coursework as well as seminars, journal clubs and a student-faculty retreat. This will be a cross-institutional training program with faculty drawn from departments ranging from computer science and statistics to genetics and medicine, in five participating institutions in the Gulf Coast Consortia in the Houston Area.The training of scientists equipped to manage and extract information from large data sets will greatly facilitate biological discovery in areas such as infectious disease and cancer and therefore this training program will have a direct, positive impact on public health.