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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cohen, W.: Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 80–89. Morgan Kaufmann, Lake Tahoe (1995)
GenBank Overview, http://www.ncbi.nlm.nih.gov/Genbank/index.html
Hatzigeorgiou, A.: Translation Initiation Start Prediction in Human cDNAs with High Accuracy. Bioinformatics 18(2), 343–350 (2002)
John, G.H., Langley, P.: Estimating Continuous Distributions in Bayesian Classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann, San Francisco (1995)
Kent Ridge Bio-medical Data Set Repository, http://sdmc.i2r.a-star.edu.sg/rp/
Kozak, M.: An Analysis of 5’-Noncoding Sequences from 699 Vertebrate Messenger RNAs. Nucleic Acids Research 15(20), 8125–8148 (1987)
Kozak, M.: The Scanning Model for Translation: An Update. The Journal of Cell Biology 108(2), 229–241 (1989)
Kozak, M.: Initiation of Translation in Prokaryotes and Eukaryotes. Gene 234(2), 187–208 (1999)
Kozak, M., Shatkin, A.J.: Migration of 40 S Ribosomal Subunits on Messenger RNA in the Presence of Edeine. Journal of Biological Chemistry 253(18), 6568–6577 (1978)
Liu, H., Han, H., Li, J., Wong, L.: Using Amino Acid Patterns to Accurately Predict Translation Initiation Sites. Silico Biology 4(3), 255–269 (2004)
Liu, H., Wong, L.: Data Mining Tools for Biological Sequences. Journal of Bioinformatics and Computational Biology 1(1), 139–168 (2003)
Nishikawa, T., Ota, T., Isogai, T.: Prediction whether a Human cDNA Sequence Contains Initiation Codon by Combining Statistical Information and Similarity with Protein Sequences. Bioinformatics 16(11), 960–967 (2000)
Pedersen, A.G., Nielsen, H.: Neural Network Prediction of Translation Initiation Sites in Eukaryotes: Perspectives for EST and Genome analysis. In: Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology, pp. 226–233. AAAI Press, Menlo Park (1997)
Peri, S., Pandey, A.: A Reassessment of the Translation Initiation Codon in Vertebrates. Trends in Genetics 17(12), 685–687 (2001)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Salamov, A.A., Nishikawa, T., Swindells, M.B.: Assessing Protein Coding Region Integrity in cDNA Sequencing Projects. Bioinformatics 14(5), 384–390 (1998)
Stormo, G.D., Schneider, T.D., Gold, L., Ehrenfeucht, A.: Use of the ’Perceptron’ Algorithm to Distinguish Translational Initiation Sites in E. coli. Nucleic Acids Research 10(9), 2997–3011 (1982)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Zeng, F., Yap, H., Wong, L.: Using Feature Generation and Feature Selection for Accurate Prediction of Translation Initiation Sites. In: Proceedings of the 13th International Conference on Genome Informatics, Tokyo, Japan, pp. 192–200 (2002)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tzanis, G., Berberidis, C., Alexandridou, A., Vlahavas, I. (2005). Improving the Accuracy of Classifiers for the Prediction of Translation Initiation Sites in Genomic Sequences. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_40
Download citation
DOI: https://doi.org/10.1007/11573036_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29673-7
Online ISBN: 978-3-540-32091-3
eBook Packages: Computer ScienceComputer Science (R0)