Information Integration using Entity-Relations across Bioinformatics Sources
Project Summary The functional analysis of genes using genomes is a major research issue in modern biology. Functional analysis is the study of how a biological entity is functionally related to other biological entities. Typical questions might include: does gene X encode chemical Y in organism Z? Does gene X regulate behavior B? Which other genes regulate the same behavior in other organisms? Information integration is necessary to perform successful functional analysis, since biologists must compare data from multiple sources of different types to answer such questions. Such data integration for functional analysis is still extremely difficult. It is carried out largely by hand, in a labor intensive and error prone process, in literature citations and genome annotations. Expert database curators can tag the necessary entities and relations, but the scale of such human efforts is far less than even the current scale of the available information. A typical search query for bioinformatics sources today is at the level of words and phrases. This is not a high enough level conceptually to enable the comparisons across sources necessary for functional analysis. Most bioin-formatics research is focused upon this data level, trying to find patterns directly within the genome sources such as sequences or within the biomedical literature. Most of the rest of bioinformatics research is focused upon jumping directly to the knowledge level, trying to find patterns using hand-encoded special purpose knowledge. Expert users of expert systems can easily retrieve facts, but the human labor involved in developing such expert systems limits the number of such systems to a small fraction of the available biological knowledge. Information integration can support search queries at far more deeper level than data integration across a far broader set of sources than knowledge integration. That is, information management aims to be close to as scalable as database management, while at the same time being as close to semantic as knowledge management. There is a very wide range of user needs in this middle ground of information in-between data and knowledge. The same technologies will be applicable across many other subject areas in science and beyond. The information level for text documents is focused around entities, in-between words for data and facts for knowledge. That is, we will base our technologies upon automatic techniques to extract phrases from general texts and to tag the entities represented by these phrases. For biology, the entities are objects …