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Predicting Protein Localization Using a Domain Adaptation Approach

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Book cover Biomedical Engineering Systems and Technologies (BIOSTEC 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 452))

Abstract

A challenge arising from the ever-increasing volume of biological data generated by next generation sequencing technologies is the annotation of this data, e.g. identification of gene structure from the location of splice sites, or prediction of protein function/localization. The annotation can be achieved by using automated classification algorithms. Supervised classification requires large amounts of labeled data for the problem at hand. For many problems, labeled data is not available. However, labeled data might be available for a similar, related problem. To leverage the labeled data available for the related problem, we propose an algorithm that builds a naïve Bayes classifier for biological sequences in a domain adaptation setting. Specifically, it uses the existing large corpus of labeled data from a source organism, in conjunction with any available labeled data and lots of unlabeled data from a target organism, thus alleviating the need to manually label a large number of sequences for a supervised classifier. When tested on the task of predicting protein localization from the composition of the protein, this algorithm performed better than the multinomial naïve Bayes classifier. However, on a more difficult task, of splice site prediction, the results were not satisfactory.

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Notes

  1. 1.

    Downloaded from http://www.psort.org/dataset/datasetv2.html

  2. 2.

    Downloaded from http://www.cbs.dtu.dk/services/TargetP/datasets/datasets.php

  3. 3.

    Downloaded from ftp://ftp.tuebingen.mpg.de/fml/cwidmer/

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Acknowledgements

The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, and MRI-1126709.

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Authors and Affiliations

  1. Kansas State University, Manhattan, KS, 66506, USA

    Nic Herndon & Doina Caragea

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  1. Nic Herndon
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  2. Doina Caragea
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Correspondence to Nic Herndon .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Mireya Fernández-Chimeno

  2. Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Pedro L. Fernandes

  3. Boston College, Chestnut Hill, Massachusetts, USA

    Sergio Alvarez

  4. University of Guelph, Guelph, Canada

    Deborah Stacey

  5. University of Vic, Vic, Spain

    Jordi Solé-Casals

  6. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. New University of Lisbon, Lisboa, Portugal

    Hugo Gamboa

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Herndon, N., Caragea, D. (2014). Predicting Protein Localization Using a Domain Adaptation Approach. In: Fernández-Chimeno, M., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2013. Communications in Computer and Information Science, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44485-6_14

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  • DOI: https://doi.org/10.1007/978-3-662-44485-6_14

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  • Online ISBN: 978-3-662-44485-6

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