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

生命科学研究社区产生了大量关于基因、蛋白质、序列等的数据。这些数据被收集在可公开获得的资源中，如Entrez gene、PDB和PubMed，以及在重点收集的资源中，如TAIR和OMIM。许多本体，如GO、PO和UMLS都在使用，以提高互操作性。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(CV)术语进行注释。记录经常被超链接到其他存储库中的记录，创建了一个经过精心策划的语义知识的生物网络。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，以发现有意义的模式。该方法建立在跨多个本体的简历术语对之间找到潜在的有意义的和新颖的关联。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。其目的是确定具有生物意义的关联，产生可操作的知识块，供科学家使用，以及一系列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。数据来自众多来源，包括基因、基因注释、本体论和文献。这项研究的探索性(急切)既涉及生物学学科，也涉及计算机科学学科。从生物学的角度来看，高度的投机与任何已发现的生物模式有关。发现的模式并不一定符合实验验证的标准。该研究方法结合了来自多个计算机科学子学科的算法和分析技术。虽然预计会有具体的技术创新，但需要定义一组相互关联的计算机科学挑战。这项研究具有更广泛影响的潜力，因为该方法可以应用于生物语义网上任何类型的链接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的合作项目。有关更多信息，请参阅以下URL:http://www.umiacs.umd.edu/research/CLIP/RSEAGER2009/上的项目网页

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/