A Holistic Approach to Information Processing for Biomedical Networks

描述（由申请人提供）： 申请人描述了一个为期5年的职业发展计划，导致在Marco Ramoni和共同导师大卫贝茨，艾萨克Kohane和彼得Szolovits下的生物医学网络的独立学术研究。申请人提出了一个综合的培训和研究计划，以导致独立。培训涉及申请人正在调查的网络所依据的生物医学方法。在研究方面，假设生物医学网络中信息处理的整体方法能够发现通过当前还原论方法无法识别的新型关系。目的是：1）设计和开发一个方法框架，基于统计信号处理，能够分析 生物医学网络和发现隐藏的关系。2)开发和实施可扩展的模块化 能够自适应地将信息集成到目标1所述框架中的架构。3)将具体目标2中的体系结构应用于大规模基于EHR的临床信息学网络分析系统的开发。

DESCRIPTION (provided by applicant): The applicant describes a 5-year career development program leading to independent academic research in biomedical networks under Marco Ramoni and co-mentors David Bates, Isaac Kohane, and Peter Szolovits. The applicant proposes a combined training and research program to lead to independence. Training involves biomedical methods underlying the networks being investigated by the applicant. On the research front, the hypothesis is that a holistic approach to the processing of information in biomedical networks is able to uncover novel types of relationships that could not be identified through current reductionistic methods. The aims are: 1) Design and develop a methodological framework, based on statistical signal processing, able to analyze biomedical networks and discover hidden relationships. 2) Develop and implement a scalable, modular architecture that is able to adaptively integrate information into the framework described in aim 1. 3) Apply the architecture in specific aim 2 to the development of a system for the analysis of large-scale EHR-based clinical informatics networks.