III EAGER Collaborative Research: Exploratory Research on the Annotated Biological Web

生命科学研究界产生了大量关于基因、蛋白质、序列等方面的数据。这些信息可从公共资源中获取，如Entrez Gene、PDB和PubMed，以及集中收集，如TAIR和OMIM。许多本体（如GO、PO和UMLS）被用于提高互操作性。这些资源中的记录通常使用来自一个或多个本体的受控词汇表（CV）术语进行注释。记录通常与其他存储库中的记录进行超链接，从而创建了一个精心策划的语义知识生物网络。这个项目的目标是开发工具来探索和挖掘这个由注释和超链接条目组成的丰富网络，从而发现有意义的模式。该方法建立在寻找跨多个本体的CV术语对之间潜在有意义和新颖的关联的基础上。跨本体的关联桥梁反映了跨存储库的注释实践。人们正在探索各种图形数据挖掘和网络分析技术，以发现跨多个本体的CV术语组的复杂模式。其目的是确定生物学上有意义的关联，这些关联可以产生可操作的知识，并提供给科学家，同时还有一组支持已确定模式的黄金出版物。该项目的智力价值在于，与其他生物信息学数据集成和分析项目相比，它是独一无二的。数据集成来自多个来源，包括基因、基因注释、本体论和文献。本研究的探索性（EAGER）是关于生物和计算机科学学科的。从生物学的观点来看，任何发现的生物模式都与高度的推测有关。发现的模式不一定符合实验验证的标准。研究方法结合了多个计算机科学分支学科的算法和分析技术。虽然预计会有具体的技术创新，但需要定义一套相互关联的计算机科学挑战。这项研究具有潜在的更广泛的影响，因为该方法可以应用于生物语义网上任何类型的相互链接的资源，以及任何超链接资源的集合。这项研究是马里兰大学和爱荷华大学合作进行的。欲了解更多信息，请参见以下网址的项目网页：http://www.umiacs.umd.edu/research/CLIP/RSEAGER2009/

The life science research community generates an abundance of data on genes, proteins, sequences, etc. These are captured in publicly available resources such as Entrez Gene, PDB and PubMed and in focused collections such as TAIR and OMIM. A number of ontologies such as GO, PO and UMLS are in use to increase interoperability. Records in these resources are typically annotated with controlled vocabulary (CV) terms from one or more ontologies. Records are often hyperlinked to those in other repositories, creating a richly curated biological Web of semantic knowledge. The objective of this project is to develop tools to explore and mine this rich Web of annotated and hyperlinked entries so as to discover meaningful patterns.The approach builds upon finding potentially meaningful and novel associations between pairs of CV terms cross multiple ontologies. The bridge of associations across ontologies reflects annotation practices across repositories. A variety of graph data mining and network analysis techniques are being explored to find complex patterns of groups of CV terms cross multiple ontologies. The intent is to identify biologically meaningful associations that yield nuggets of actionable knowledge to be made available to the scientist together with a set of golden publications that support the identified patterns.The intellectual merit of the project is that it is unique in comparison to other bioinformatics data integration and analysis projects. Data is integrated from across numerous sources including genes, gene annotations, ontologies, and the literature. The exploratory nature (EAGER) of this research is both with respect to the biological and the computer science disciplines. From the biological viewpoint, a high level of speculation is associated with any discovered biological patterns. Discovered patterns night not necessarily meet criteria for experimental validation. The research methodology combines algorithmic and analytical techniques from multiple computer science sub-disciplines. While specific technical innovations are expected, an inter-related set of computer science challenges needs to be defined.This research has the potential for broader impact since the methodology can be applied to any type of interlinked resources on the biological semantic Web as well as to any collection of hyperlinked resources. This research is a collaboration between the University of Maryland and the University of Iowa. For further information see the project web pages at the following URL:http://www.umiacs.umd.edu/research/CLIP/RSEAGER2009/