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Genomics information retrieval using a Bayesian model for learning and re-ranking

Published: 02 August 2010 Publication History

Abstract

The use of large-scale experimental techniques and biomedical tools has increased the pace at which biologists produce useful information. This promotes us to propose a Bayesian model for learning and re-ranking to boost genomics information retrieval performance. We first describe a general model for discovering the property of each passage. Then, we examine a Bernoulli distribution as the prior distribution and provide an efficient way to obtain the training passages for parameter estimation, according to the characterizations of the Bernoulli distribution. Later, we evaluate our proposed model by conducting extensive experiments on the TREC 2007 and 2006 Genomics data sets. The experimental results show the effectiveness of the proposed model for improving performance on two years' TREC Genomics data sets. Furthermore, the conclusions and future prospects are also discussed.

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  1. Genomics information retrieval using a Bayesian model for learning and re-ranking

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    cover image ACM Conferences
    BCB '10: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
    August 2010
    705 pages
    ISBN:9781450304382
    DOI:10.1145/1854776
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    Published: 02 August 2010

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    Author Tags

    1. Bayesian inference
    2. Bernoulli distribution
    3. Model
    4. genomics
    5. information retrieval

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