General-purpose search techniques for genomic text.
Fast and accurate techniques for searching large genomic text collections are becoming increasingly important. While Information Retrieval is well-established for general-purpose text retrieval tasks, less is known about retrieval techniques for genomic text data. In this paper, we investigate and propose general-purpose search techniques for genomic text. In particular, we show that significant improvements can result from manual term expansion, where additional words are added to queries and documents. We also show that collection partitioning, where documents are included in or excluded from the search space, is highly effective for some tasks. We experiment with our techniques on four text collections and show, for example, that the collection partitioning scheme can improve effectiveness by almost 9.5% over a standard retrieval baseline. We conclude by recommending techniques that can be considered for most genomic search tasks.
Chattaraj
Abhijit
A
School of Computer Science and IT, RMIT University, GPO Box 2476V, Melbourne, 3001, Australia. abhijit@cs.rmit.edu.au
Williams
Hugh E
HE
Cannane
Adam
A
D003628:Database Management Systems; D016206:Databases, Bibliographic / Q000639:trends*; D023281:Genomics / Q000379:methods* / Q000639:trends*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010506:Periodicals as Topic; D016415:Sequence Alignment; D018511:Systems Integration