Automatic Discovery and Inferencing of Complex Bioinformatics Web Interfaces

  • Authors:

  • Anne H. Ngu;Daniel Rocco;Terence Critchlow;David Buttler

  • Affiliations:

  • Department of Computer Science, Texas State University, San Marcos, USA 78666;Department of Computer Science, University of West Georgia, Carollton, USA 30118;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, USA 94551;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, USA 94551

  • Venue:
  • World Wide Web
  • Year:
  • 2005

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Abstract

The World Wide Web provides a vast resource to genomics researchers, with Web-based access to distributed data sources such as BLAST sequence homology search interfaces. However, finding the desired scientific information can still be very tedious and frustrating. While there are several known servers on genomic data (e.g., GeneBank, EMBL, NCBI) that are shared and accessed frequently, new data sources are created each day in laboratories all over the world. Sharing these new genomics results is hindered by the lack of a common interface or data exchange mechanism. Moreover, the number of autonomous genomics sources and their rate of change outpace the speed at which they can be manually identified, meaning that the available data is not being utilized to its full potential. An automated system that can find, classify, describe, and wrap new sources without tedious and low-level coding of source-specific wrappers is needed to assist scientists in accessing hundreds of dynamically changing bioinformatics Web data sources through a single interface. A correct classification of any kind of Web data source must address both the capability of the source and the conversation/interaction semantics inherent in the design of the data source. We propose a service class description (SCD)-a meta-data approach for classifying Web data sources that takes into account both the capability and the conversational semantics of the source. The ability to discover the interaction pattern of a Web source leads to increased accuracy in the classification process. Our results show that an SCD-based approach successfully classifies two thirds of BLAST sites with 100% accuracy and two thirds of bioinformatics keyword search sites with around 80% precision.