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This article is part of the supplement: Proceedings of the Neural Information Processing Systems (NIPS) Workshop on Machine Learning in Computational Biology (MLCB)

Open Access Research

Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

Cornelia Caragea12*, Doina Caragea3, Adrian Silvescu12 and Vasant Honavar12

Author Affiliations

1 Artificial Intelligence Research Laboratory, Department of Computer Science,Iowa State University, Ames, IA, 50010, USA

2 Center for Computational Intelligence, Learning, and Discovery, Iowa State University, Ames, IA, 50010, USA

3 Computer and Information Sciences, Kansas State University, Manhattan, KS, 65501, USA

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BMC Bioinformatics 2010, 11(Suppl 8):S6  doi:10.1186/1471-2105-11-S8-S6

Published: 26 October 2010

Abstract

Background

Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data.

Results

In this paper, we present an Abstraction Augmented Markov Model (AAMM) based approach to semi-supervised protein subcellular localization prediction problem. We investigate the effectiveness of AAMMs in exploiting unlabeled data. We compare semi-supervised AAMMs with: (i) Markov models (MMs) (which do not take advantage of unlabeled data); (ii) an expectation maximization (EM); and (iii) a co-training based approaches to semi-supervised training of MMs (that make use of unlabeled data).

Conclusions

The results of our experiments on three protein subcellular localization data sets show that semi-supervised AAMMs: (i) can effectively exploit unlabeled data; (ii) are more accurate than both the MMs and the EM based semi-supervised MMs; and (iii) are comparable in performance, and in some cases outperform, the co-training based semi-supervised MMs.