Training in Biomedical Discovery from Large Scale Data Sets

基因组学、蛋白质组学和先进的成像技术的发展导致了 积累了大量的生物数据。随着大规模数据集在 生物医学研究，我们正在接近一种范式转变，在这种转变中，发现的过程是由数据驱动的， 其中数据既是假说的来源，也是检验假说的手段。这些海量数据 是丰富的信息来源；然而，提取有意义的信息可能是一项艰巨的挑战，并且 通常会给发现过程带来瓶颈。因此，迫切需要跨学科 对了解数据、如何生成数据以及数据用途的科学家进行培训。在……里面 此外，这些科学家必须成为新计算工具的开发者和高技能用户 对于分析大型数据集是必要的。这个项目的目标是培养学生熟练地使用 主要包括以下几个方面：1.数据采集。这将包括基因组学、蛋白质组学和 成像。2.计算。这将包括数学和统计算法的知识， 有效的计算机代码的实施以及对数据仓库方法的重视 关系数据库、演绎数据库和其他数据库。3.数据集成。这是一个关键领域，涉及提取 来自不同空间和时间尺度上的不同数据集的有用信息。它将包括 具有从分子到生物层面的系统建模和模拟方法的知识。 课程还将侧重于计算数据挖掘方法。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，杂志俱乐部和师生休养生息。这将是 跨机构培训计划，教师来自计算机科学等多个部门 以及遗传学和医学的统计数据，在墨西哥湾沿岸财团的五个参与机构中 对有能力管理和从大数据集中提取信息的科学家的培训将 极大地促进了传染病和癌症等领域的生物发现，因此 培训计划将对公众健康产生直接、积极的影响。

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.