SGER: Belief Networks for Human Pathways

摘要：目前，对人类精神疾病等复杂主题的分析被分散到多个科学团体中，这些团体往往很少或根本没有相互作用。很有可能，甚至很有可能，现在可以找到强有力的线索来寻找治疗许多人类疾病的方法，只要把不同的知识拼图组合在一起。用定量的概率模型来代替无序的事实集合，从而允许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，这将是更可取的。长期研究计划将解决许多问题，例如汇编数据，将交互转化为信念以及放大与每个节点相关的数据。这一设想的可行性测试从一个众所周知的系统开始，即面包师中的细胞周期网络。S酵母以信念网络形式再现已知的酵母表型效应。本分析的目标是(1)定义应用于路径数据的信念网络方法的适用性边界，(2)证明该方法的可行性，以及(3)使用所得模型作为更大规模研究的原理证明。下一步将包括用信念网络建模，这是几年来在人类自闭症方面积累的知识，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，结合专家输入和通过网络推理的实验。PI和一名研究生将参与该项目。

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.