Scalable, updatable predictive models for sequence data

Neeraj Koul, Ngot Bui, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machine learning algorithms. In particular, sequence data is being made available at a rapid rate. In many applications, the learning algorithm may not have direct access to the entire dataset because of a variety of reasons such as massive data size or bandwidth limitation. In such settings, there is a need for techniques that can learn predictive models (e.g., classifiers) from large datasets without direct access to the data. We describe an approach to learn from massive sequence datasets using statistical queries. Specifically we show how Markov Models and Probabilistic Suffix Trees (PSTs) can be constructed from sequence databases that answer only a class of count queries. We analyze the query complexity (a measure of the number of queries needed) for constructing classifiers in such settings and outline some techniques to minimize the query complexity. We also show how some of the models can be updated in response to addition or deletion of subsets of sequences from the underlying sequence database.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages681-685
Number of pages5
DOIs
StatePublished - Dec 1 2010
Event2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China
Duration: Dec 18 2010Dec 21 2010

Publication series

NameProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Other

Other2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
CountryChina
CityHong Kong
Period12/18/1012/21/10

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

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