| Title: |
Improving the Accuracy of Classifiers for the Prediction of Translation Initiation Sites in Genomic Sequences |
| Author(s): |
G. Tzanis, C. Berberidis, A. Alexandridou, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (11 pages).
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| Keywords: |
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| Appeared in: |
10th Panhellenic Conference on Informatics (PCI'2005), P. Bozanis and E.N. Houstis (Eds.), Springer-Verlag, LNCS 3746, pp. 426-436, Volos, Greece, 11-13 November, 2005.
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| Abstract: |
The prediction of the Translation Initiation Site (TIS) in a genomic sequence is an important issue in biological research. Although several methods have been proposed to deal with this problem, there is a great potential for the improvement of the accuracy of these methods. Due to various reasons, including noise in the data as well as biological reasons, TIS prediction is still an open problem and definitely not a trivial task. In this paper we follow a three-step approach in order to increase TIS prediction accuracy. In the first step, we use a feature generation algorithm we developed. In the second step, all the candidate features, including some new ones generated by our algorithm, are ranked according to their impact to the accuracy of the prediction. Finally, in the third step, a classification model is built using a number of the top ranked features. We experiment with various feature sets, feature selection methods and classification algorithms, compare with alternative methods, draw important conclusions and propose improved models with respect to prediction accuracy. |
| See also : |
Springer-Verlag
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This paper has been cited by the following:
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C. Ma, D. Zhou, and Y. Zhou. Feature Mining and Integration for Improving the Prediction Accuracy of Translation Initiation Sites in Eukaryotic mRNAs. In Proceedings of the 5th International Conference on Grid and Cooperative Computing Workshops, IEEE Computer Society, pp. 349-356, 2006. |
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| 3 |
Z. Yuntao, D. Baomiao, Application of RS-GA-SVM method in the recognition of translation initiation sites of eukaryotic DNAs. Computers and Applied Chemistry. 24(6), pp. 794-798, 2007. |
| 4 |
C. N. Nobre, J. M. Ortega, A. de Padua Braga. High Efficiency on Prediction of Translation Initiation Site (TIS) of RefSeq Sequences. In Proceedings of the Second Brazilian Symposium on Bioinformatics (BSB 2007), Springer-Verlag, pp. 138-148, 2007 |
| 5 |
T. Gao, Y. Tian, X. Shao, N. Deng, “Accurate Prediction of Translation Initiation Sites by Universum SVM”, 2nd International Symposium on Optimization and Systems Biology, pp. 279–286, China, 2008. |
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J. Lee, C. M. Steele, T. Chau, “Swallow segmentation with artificial neural networks and multi-sensor fusion”, Medical Engineering and Physics, 31(9), pp. 1049-1055, 2009. |
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