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

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Article discussing the semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models.

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13 p.

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Caragea, Cornelia; Caragea, Doina; Silvescu, Adrian & Honavar, Vasant October 26, 2010.

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Article discussing the semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models.

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13 p.

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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 approach to semi-supervised training of MMs (that make used 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.

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  • BMC Bioinformatics, 2010, London: BioMed Central Ltd.

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  • Publication Title: BMC Bioinformatics
  • Volume: 11
  • Issue: Suppl 8
  • Peer Reviewed: Yes

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Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • October 26, 2010

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  • Sept. 6, 2013, 3:22 p.m.

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  • May 23, 2014, 2:27 p.m.

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Caragea, Cornelia; Caragea, Doina; Silvescu, Adrian & Honavar, Vasant. Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models, article, October 26, 2010; [London, United Kingdom]. (digital.library.unt.edu/ark:/67531/metadc180947/: accessed August 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.