INGOT: a family of statistical computing algorithms for hypothesis-driven imaging genomic and longitudinal neuroimaging analysis

 描述(由申请人提供) 这份K01大数据科学指导职业发展奖的申请书描述了宾夕法尼亚大学神经放射学家J·埃里克·施密特博士提出的一项建议。它建议开发集成的神经成像基因组本体论工具箱(INGIN)，这是一个免费的、开放源码的分析工具集合，用于结合分析成像和基因组数据。这些工具将包括一条用于组合成像基因组分析的自动化管道，将基因和神经解剖学本体论数据库整合到分析框架中，并纳入结构方程模型，以促进对遗传信息丰富的纵向数据集的分析。然后，这些工具将通过模拟进行测试，并随后应用于两个具有遗传信息的大型神经成像数据集。该提案还详细介绍了施密特博士从神经放射学研究员过渡到独立成像基因组学研究员和神经放射学教职员工期间的持续职业发展。描述了严格的跨学科课程，旨在加强和更新先前在定量神经成像和基因组学方面的培训，保持临床神经放射学的现有技能，并通过研讨会、课程和研讨会相结合的方式发展生物信息学和统计学的强大技术技能。

 DESCRIPTION (provided by applicant) This application for K01 Mentored Career Development Award in Big Data Science describes a proposal from Dr. J. Eric Schmitt MD, PhD, a neuroradiologist at the University of Pennsylvania. It proposes the development the Integrated Neuroimaging Genomic Ontology Toolbox (INGOT), a free, open source collection of analytic tools for the combined analysis of imaging and genomic data. These tools will include an automated pipeline for combined imaging genomic analysis, the integration of gene and neuroanatomic ontology databases into the analytic framework, and the incorporation of structural equation models to facilitate analysis of genetically- informative longitudinal datasets. These tools will then be tested via simulation and subsequently applied to two large genetically-informative neuroimaging datasets. The proposal also details the continued career development of Dr. Schmitt as he transitions from Neuroradiology Fellow to independent imaging genomics researcher and neuroradiology faculty. A rigorous interdisciplinary curriculum is described, intended to reinforce and update prior training in quantitative neuroimaging and genomics, maintain existing skills in clinical neuroradiology and develop a strong technical skill set in bioinformatics and statistics via a combination of workshops, coursework, and seminars.