SGER: Belief Networks for Human Pathways

SGER：人类道路的信念网络

• 批准号: 0438291
• 负责人: Andrey Rzhetsky
• 金额: $ 10万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0438291&HistoricalAwards=false
• 关键词: SGER; Belief; Networks; Human; Pathways

ABSTRACTNSF-0438921RZHETSKY, ANDREYAnalysis of complex subjects, such as human psychiatric disorders, is currently fragmented into multiple scientific communities, which often have little or none interactions. It is quite possible, even likely, that powerful cues to finding remedies to numerous human maladies can be found right now provided that disparate pieces of the knowledge puzzle are combined in one head. It would be even more desirable to have the unordered collection of facts substituted with a quantitative probabilistic model allowing for formal evaluations of model predictions, analysis of discrepancies between data points, and hypothesis testing. The long-term research plan would address many issues, such as compiling the data, converting interactions into beliefs and amplifying the data associated with each node. The test of the feasibility of this vision begins with a well-known system, cell-cycle network in the baker.s yeast to reproduce with belief network formalism the known phenotypic effects for yeast. The goals in this analysis would be (1) define applicability boundaries of the belief network methodology as applied to pathway data, (2) demonstrate feasibility of the approach, and (3) use the resulting model as a proof-of-principle for a larger study. The next step would include modeling with belief networks the knowledge that has been compiled over a few years on autism in humans, with a focus on automated conversion of molecular interaction data into belief networks, computation of probabilities for individual interactions, incorporation of expert inputs and experiments with reasoning over the network. The PI and a graduate student will be engaged in this project.

对复杂主题的分析，如人类精神疾病，目前被分散到多个科学社区，这些社区通常很少或根本没有相互作用。 很有可能，甚至很有可能，现在就可以找到强大的线索来寻找人类众多疾病的治疗方法，只要知识拼图的不同部分结合在一个头脑中。 更可取的是，用定量概率模型代替无序的事实集合，以便对模型预测进行正式评估，分析数据点之间的差异，并进行假设检验。 长期研究计划将解决许多问题，例如汇编数据，将交互转换为信念以及放大与每个节点相关的数据。这一设想的可行性的测试开始于一个众所周知的系统，在面包酵母细胞周期网络复制与信念网络形式主义的酵母已知的表型效应。 本分析的目标是（1）定义信念网络方法应用于路径数据的适用性边界，（2）证明该方法的可行性，以及（3）使用所得模型作为更大研究的原理证明。下一步将包括用信念网络对几年来关于人类自闭症的知识进行建模，重点是将分子相互作用数据自动转换为信念网络，计算个体相互作用的概率，纳入专家输入和通过网络进行推理的实验。 PI和一名研究生将参与该项目。