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、序列等的数据，这些数据被收录在公共资源中，如Zeroz Gene、PDB和PubMed，以及TAIR和OMIM等集中收集的数据中。 许多本体，如GO，PO和UMLS正在使用，以提高互操作性。这些资源中的记录通常用来自一个或多个本体的受控词汇（CV）术语来注释。记录经常被超链接到其他存储库中的记录，创建了一个丰富的语义知识的生物网络。这个项目的目标是开发工具，探索和挖掘这个丰富的网络的注释和超链接的条目，以发现有意义的patterns.The方法建立在寻找潜在的有意义的和新颖的关联对CV术语之间的跨多个本体。跨本体的关联桥反映了跨存储库的注释实践。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，产生可操作的知识金块，以提供给科学家与一组黄金出版物，支持所确定的模式。该项目的智力价值是，它是独一无二的相比，其他生物信息学数据集成和分析项目。数据是从包括基因、基因注释、本体论和文献在内的众多来源整合而来的。这项研究的探索性质（EAGER）是关于生物和计算机科学学科。从生物学的观点来看，高度的推测与任何发现的生物模式有关。 发现的模式不一定符合实验验证的标准。研究方法结合了来自多个计算机科学子学科的算法和分析技术。虽然预计具体的技术创新，一个相互关联的一套计算机科学的挑战需要被defined.This研究有可能产生更广泛的影响，因为该方法可以应用于任何类型的生物语义网上的互联资源，以及任何超链接资源的集合。 这项研究是马里兰州大学和爱荷华州大学的合作。欲了解更多信息，请访问以下URL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/