Harvard Training Program in Bioinformatics Applied to Diabetes, Obesity and Metabolism.

摘要 在过去十年中，我们目睹了变革性的技术和分析进步 其可以以全局方式查询生物系统，通常跨越图中的选择轴。 分子光谱对基因、基因标记、转录本、蛋白质、 小分子及其相互作用产生了大量的数据集，反映了 特定代谢、发育或组织状态。这项活动越来越可行， 与疾病最相关的生物体，人类：当与丰富的临床和 观察性队列、临床试验或电子表格中的表型信息 从医学记录中，可以做出巨大的推论来得出病理生理学的见解。 这种类型的活动产生了大量与2型糖尿病、肥胖和糖尿病相关的信息。 相关代谢特征。迫切需要吸引有经验的调查人员， 数学，统计学，计算生物学和生物信息学的分析， 这些数据的解释，并装备调查人员谁有兴趣在这些 表型与提取有价值的知识所需的工具和技能。在本提案中，我们 在哈佛系统内组建了一个由30多名有成就的调查人员组成的团队 他们在每一个相关的技能组合中拥有尖端的能力， 支持对代谢性疾病的公开兴趣。这项培训补助金将利用他们的 通过资助8名选定的受训人员，为他们提供专门的 指导，并与教师导师谁可以提供严格的培训， 与NIDDK感兴趣的疾病相关的多学科环境。

ABSTRACT Over the past decade we have witnessed transformative technological and analytical advances which can query biological systems in a global manner, typically across a select axis in the molecular spectrum. The comprehensive study of genes, gene marks, transcripts, proteins, small molecules, and their interactions have generated enormous data sets that reflect a specific metabolic, developmental or tissue state. Increasingly this activity has been feasible in the organism most relevant to disease, the human: when coupled with the rich clinical and phenotyping information available in observational cohorts, clinical trials or the electronic medical record, tremendous inferences can be made to derive pathophysiological insight. This type of activity has produced a trove of information relevant to type 2 diabetes, obesity and related metabolic traits. There is an urgent need to attract investigators experienced in mathematics, statistics, computational biology and bioinformatics to the analysis and interpretations of such data, and to equip investigators who have an interest in these phenotypes with the tools and skills required to extract valuable knowledge. In this proposal, we have assembled a team of three dozen accomplished investigators across the Harvard system who have cutting-edge capabilities in each of the pertinent skill sets, and whose track record supports a declared interest in metabolic disease. This training grant will leverage their complementary expertise by funding eight selected trainees, providing them with dedicated instruction, and pairing them with faculty mentors who can provide rigorous training in a multidisciplinary setting pertinent to diseases of interest to the NIDDK.