Abstract
Supervised classifiers are highly dependent on abundant labeled training data. Alternatives for addressing the lack of labeled data include: labeling data (but this is costly and time consuming); training classifiers with abundant data from another domain (however, the classification accuracy usually decreases as the distance between domains increases); or complementing the limited labeled data with abundant unlabeled data from the same domain and learning semi-supervised classifiers (but the unlabeled data can mislead the classifier). A better alternative is to use both the abundant labeled data from a source domain and the limited labeled data from the target domain to train classifiers in a domain adaptation setting. We propose such a classifier, based on logistic regression, and evaluate it for the task of splice site prediction – a difficult and essential step in gene prediction. Our classifier achieved high accuracy, with highest areas under the precision-recall curve between 50.83% and 82.61%.
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References
Arita, M., Tsuda, K., Asai, K.: Modeling splicing sites with pairwise correlations. Bioinformatics 18(suppl 2), S27–S34 (2002)
Baten, A.K.M.A., Halgamuge, S.K., Chang, B., Wickramarachchi, N.: Biological Sequence Data Preprocessing for Classification: A Case Study in Splice Site Identification. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part II. LNCS, vol. 4492, pp. 1221–1230. Springer, Heidelberg (2007)
Baten, A.K.M.A., Chang, B.C.H., Halgamuge, S.K., Li, J.: Splice site identification using probabilistic parameters and svm classification. BMC Bioinformatics 7(suppl 5), S15 (2006)
Bernal, A., Crammer, K., Hatzigeorgiou, A., Pereira, F.: Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction. PLoS Comput. Biol. 3(3), e54 (2007)
Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C., Furey Jr., T.S., Ares, M., Haussler, D.: Knowledge-based Analysis of Microarray Gene Expression Data Using Support Vector Machines. PNAS 97(1), 262–267 (2000)
Cai, D., Delcher, A., Kao, B., Kasif, S.: Modeling splice sites with Bayes networks. Bioinformatics 16(2), 152–158 (2000)
Catal, C., Diri, B.: Unlabelled extra data do not always mean extra performance for semi-supervised fault prediction. Expert Systems 26(5), 458–471 (2009); Wiley Online Library
Chelba, C., Acero, A.: Adaptation of maximum entropy capitalizer: Little data can help a lot. Computer Speech & Language 20(4), 382–399 (2006)
Dai, W., Xue, G.R., Yang, Q., Yu, Y.: Transferring Naïve Bayes Classifiers for Text Classification. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (2007)
Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: Proceedings of the Twenty Third International Conference on Machine Learning, pp. 233–240. ACM (2006)
Gross, S.S., Do, C.B., Sirota, M., Batzoglou, S.: Contrast: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction. Genome Biology 8(12), R269 (2007)
Giannoulis, G., Krithara, A., Karatsalos, C., Paliouras, G.: Splice site recognition using transfer learning. In: Likas, A., Blekas, K., Kalles, D. (eds.) SETN 2014. LNCS (LNAI), vol. 8445, pp. 341–353. Springer, Heidelberg (2014)
Herndon, N., Caragea, D.: Empirical Study of Domain Adaptation with Naïve Bayes on the Task of Splice Site Prediction. In: Proceedings of the 5th International Conference on Bioinformatics Models, Methods and Algorithms, pp. 57–67 (2014)
Herndon, N., Caragea, D.: Predicting Protein Localization Using a Domain Adaptation Approach. In: FernÁndez Chimeno, M., Fernandes, P.L., Alvarez, S., Stacey, D., Solé-Casals, J., Fred, A., Gamboa, H. (eds.) BIOSTEC 2013. CCIS, vol. 452, pp. 191–206. Springer, Heidelberg (2014)
Hubbard, T.J., Park, J.: Fold recognition and ab initio structure predictions using hidden markov models and β-strand pair potentials. Proteins: Structure, Function, and Bioinformatics 23(3), 398–402 (1995)
Korf, I., Flicek, P., Duan, D., Brent, M.R.: Integrating genomic homology into gene structure prediction. Bioinformatics 17(suppl. 1), S140–S148 (2001)
Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics, 191–201 (1992)
Li, J.L., Wang, L.F., Wang, H.Y., Bai, L.Y., Yuan, Z.M.: High-accuracy splice site prediction based on sequence component and position features. Genet. Mol. Res. 11(3), 3432–3451 (2012)
Müller, K.-R., Mika, S., Rätsch, G., Tsuda, S., Schölkopf, B.: An Introduction to Kernel-Based learning Algorithms. IEEE Transactions on Neural Networks 12(2), 181–202 (2001)
Noble, W.S.: What is a support vector machine? Nat. Biotech. 24(12), 1565–1567 (2006)
Rätsch, G., Sonnenburg, S., Srinivasan, J., Witte, H., Müller, K.-R., Sommer, R., Schölkopf, B.: Improving the C. elegans genome annotation using machine learning. PLoS Computational Biology 3, e20 (2007)
Schweikert, G., Widmer, C., Schölkopf, B., Rätsch, G.: An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis. In: NIPS 2008, pp. 1433–1440 (2008)
Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.: Accurate Splice site Prediction Using Support Vector Machines. BMC Bioinformatics 8(suppl.10), 1–16 (2007)
Stanescu, A., Caragea, D.: Ensemble-based semi-supervised learning approaches for imbalanced splice site datasets. In: Proceedings of the 6th IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2014, pp. 432–437 (2014)
Stanescu, A., Caragea, D.: Semi-supervised self-training approaches for imbalanced splice site datasets. In: Proceedings of the 6th International Conference on Bioinformatics and Computational Biology, BICoB 2014, pp. 131–136 (2014)
Stanke, M., Waack, S.: Gene prediction with a hidden markov model and a new intron submodel. Bioinformatics 19(suppl 2), ii215–ii225 (2003)
Steijger, T., Abril, J.F., Engström, P.G., Kokocinski, F., Hubbard, T.J., Guigó, R., Harrow, J., Bertone, P., RGASP Consortium, et al.: Assessment of transcript reconstruction methods for rna-seq. Nature Methods 10(12), 1177–1184 (2013)
Tan, S., Cheng, X., Wang, Y., Xu, H.: Adapting Naïve Bayes to Domain Adaptation for Sentiment Analysis. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 337–349. Springer, Heidelberg (2009)
Zhang, Y., Chu, C.H., Chen, Y., Zha, H., Ji, X.: Splice site prediction using support vector machines with a Bayes kernel. Expert Syst. Appl. 30(1), 73–81 (2006)
Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.-R.: Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16(9), 799–807 (2000)
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Herndon, N., Caragea, D. (2015). Domain Adaptation with Logistic Regression for the Task of Splice Site Prediction. In: Harrison, R., Li, Y., Măndoiu, I. (eds) Bioinformatics Research and Applications. ISBRA 2015. Lecture Notes in Computer Science(), vol 9096. Springer, Cham. https://doi.org/10.1007/978-3-319-19048-8_11
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DOI: https://doi.org/10.1007/978-3-319-19048-8_11
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