Computer System for Functional Analysis of Genomic Data

描述（由申请人提供）：在这个项目的前两个阶段，都是由国家普通医学科学研究所资助并成功实施的，我们开发了GeneWays，这是一个完全自动化的系统，可以有效地从天文数字的生物医学全文文章中提取分子相互作用的信息。该项目的下一个合乎逻辑的阶段是将该系统从计算实验室带入一个实用的，有用的，甚至是不可或缺的工具，研究人员可以使用它来解决目前在实验医学和生物学中提出的复杂问题。我们在GeneWays上的工作的中心假设是，我们的计算工具将产生质量足够高的生物预测，生物医学界将投资于严肃的实验验证。具体而言，我们提出以下建议。1. 显著提高GeneWays系统的查准率和查全率。2. 我们将开发并实现一个概率信念网络的形式主义。一种与贝叶斯网络形式主义相关的信念图，它允许我们在图的顶点和边缘上放置和更新信念，以便对GeneWays数据库中的大量事实进行概率推理。我们将开发和实现一个协调的方法集合，用于计算和更新信念图的单个节点和边缘上的信念。3. 我们将开发并实施一个数学框架，将途径信息纳入遗传连锁分析的形式体系中，使每个途径知识都包含特定程度的置信度。4. 我们将处理大量的文本，如开放获取的生物医学期刊、PubMed摘要和GeneWays语料，从而建立一个全面的GeneHighWays数据库。我们将通过网络界面使学术研究人员可以轻松、免费地访问GeneHighWays数据库。我们将评估新版本的GeneWays系统和GeneHighWays数据库的数据质量、数学方法的性能和接口的质量。

DESCRIPTION (provided by applicant): In two previous stages of this project, both funded by the National Institute of General Medical Sciences and carried out successfully, we developed GeneWays, a completely automated system that efficiently distills information about molecular interactions from an astronomical number of full-text biomedical articles. The next logical stage of the project is to carry this system from the computational laboratory into a practical, useful, and even indispensable tool that researchers can use to solve complex problems currently posed in experimental medicine and biology. The central hypothesis of our work on GeneWays has been that our computational tools will generate biological predictions of a quality sufficiently high that the biomedical community will invest in serious experimental validation. Specifically, we propose the following. 1. We will improve significantly the precision and recall of the GeneWays system. 2. We will develop and implement a probabilistic belief-network formalism?a belief-graph relative of the Bayesian network formalism that allows us to place and update beliefs on both the vertices and the edges of the graph for probabilistic reasoning over the large collection of facts in the GeneWays database. We will develop and implement a coordinated collection of methods for computing and updating beliefs on individual nodes and edges of the belief graph. 3. We will develop and implement a mathematical framework for incorporating pathway information into a genetic- linkage analysis formalism in such a way that each piece of pathway knowledge includes a specified degree of confidence. 4. We will process an enormous collection of texts, such as open-access biomedical journals, PubMed abstracts, and the GeneWays corpus, and thus will build a comprehensive GeneHighWays database. We will make the GeneHighWays database easily and freely accessible to academic researchers through a web interface. We will evaluate the new version of the GeneWays system and the GeneHighWays database for the quality of data, performance of the mathematical methods, and quality of the interface.