Computer System for Functional Analysis of Genomic Data

描述（由申请人提供）：在该项目的前两个阶段，都由国家普通医学科学研究所资助并成功实施，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中提取有关分子相互作用的信息全文生物医学文章。该项目的下一个逻辑阶段是将该系统从计算实验室转化为实用，有用甚至不可或缺的工具，研究人员可以使用该工具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上工作的中心假设是，我们的计算工具将产生足够高质量的生物预测，生物医学界将投资于认真的实验验证。具体而言，我们提出以下建议。1.我们将显著提高GeneWays系统的查准率和查全率。2.我们将开发和实现概率的信念网络形式主义？一个相对于贝叶斯网络形式主义的信念图，它允许我们在GeneWays数据库中的大量事实集合上放置和更新图的顶点和边缘上的信念。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各个节点和边缘上的信念。3.我们将开发和实现一个数学框架，将途径信息纳入遗传连锁分析形式主义，以这种方式，每一条途径的知识，包括一个特定程度的信心。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语料库，从而建立一个全面的GeneHighWays数据库。我们将使GeneHighWays数据库通过网络界面方便和免费访问学术研究人员。我们将评估新版GeneWays系统和GeneHighWays数据库的数据质量、数学方法的性能和界面质量。

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.