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E-Coli Promoter Recognition Using Neural Networks with Feature Selection

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Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3645))

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Abstract

This paper investigates the effects on neural classification performance of biological data by features selection. Where the Relief-F and Symmetrical Tau feature selection algorithms were employed on a set of high level features of DNA and structural profiles. It was observed that even with a small percentage of the features used in neural classifiers, the recognition rate of E.coli promoters was not degraded significantly.

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References

  1. Xing, E.P., Jordan, M.I., Karp, R.M.: Feature Selection for High-Dimensional Genomic Microarray Data. In: Proc. 18th International Conf. on Machine Learning, pp. 601–608. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  2. Bellman, R.E.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)

    MATH  Google Scholar 

  3. Molina, L.C., Belanche, L., Nebot, A.: Feature Selection Algorithms: A Survey and Experimental Evaluation. In: Proc. of the International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, pp. 306–313. IEEE Computer Society, Los Alamitos (2002) ISBN 0-7695-1754-4

    Google Scholar 

  4. Chuzhanova, N., Jones, A., Margetts, S.: Feature selection for genetic sequence classification. Bioinformatics 14, 139–143 (1998)

    Article  Google Scholar 

  5. Saeys, Y., Degroeve, S., Aeyels, D., Van de Peer, Y., Rouze, P.: Fast feature selection using a simple Estimation of Distribution Algorithm: A case study on splice site prediction. Bioinformatics 19, 179–188 (2003)

    Article  Google Scholar 

  6. Park, C., Cho, S.B.: Genetic Search for Optimal Ensemble of Feature-Classifier Pairs in DNA Gene Expression Profiles. In: Proceedings of the International Joint Conference on Neural Networks 2003, vol. 3, pp. 1702–1707 (2003)

    Google Scholar 

  7. Hawley, D.K., McClure, W.R.: Compilation and analysis of Escherichia coli promoter DNA sequences. Nucl. Acids. Res. 11, 2237–2255 (1983)

    Article  Google Scholar 

  8. Harley, C.B., Reynolds, R.P.: Analysis of E. coli promoter sequences. Nucl. Acids. Res. 15, 2334–2361 (1987)

    Article  Google Scholar 

  9. Lisser, S., Margalit, H.: Compilation of E. coli mRNA promoter sequences. Nucl. Acids. Res. 21, 1507–1516 (1993)

    Article  Google Scholar 

  10. Mahadevan, I., Ghosh, I.: Analysis of E. coli Promoter Structures using Neural Networks. Nucl. Acids. Res. 22, 2158–2165 (1994)

    Article  Google Scholar 

  11. Ma, Q., Wang, J.T.L., Shasha, D., Wu, C.H.: DNA Sequence Classification via an Expectation Maximization Algorithm and Neural Networks: A Case Study. IEEE Transactions on Systems, Man and Cybernetics, Part C 31, 468–475 (2001)

    Article  Google Scholar 

  12. Ma, Q., Wang, J.T.L., Gattiker, J.R.: 30. In: Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks in Handbook of Massive Data Sets, pp. 1141–1168. Kluwer Academic Publishers, Dordrecht (2002)

    Google Scholar 

  13. Salgado, H., Santos-Zavaleta, A., Gama-Castro, S., Millán-Zárate, D., Díaz-Peredo, E., Sánchez-Solano, F., Pérez-Rueda, E., Bonavides-Martínez, C., Collado-Vides, J.: RegulonDB (version 3.2): Transcriptional Regulation and Operon Organization in Escherichia coli K-12. Nucl. Acids. Res. 29, 72–74 (2001)

    Article  Google Scholar 

  14. Hirsh, H., Noordewier, M.: Using background knowledge to improve inductive learning of DNA sequences. In: Proceedings of the Tenth Conference on Artificial Intelligence for Applications, pp. 351–357 (1994)

    Google Scholar 

  15. Conilione, P.C., Wang, D.: Effect of Non-Target Examples on E.coli Promoters Recognition Using Neural Networks. In: Proceedings of International Joint Conference on Neural Networks IJCNN 2005. IEEE, Los Alamitos (2005) (to be published)

    Google Scholar 

  16. Lennon, G.G., Nussinov, R.: Homonyms, synonyms and mutations of the sequence/structure vocabulary. J. Mol. Biol. 175, 425–430 (1984)

    Article  Google Scholar 

  17. Kolpakov, R.M., Kucherov, G.: Finding Approximate Repetitions under Hamming Distance. In: Meyer auf der Heide, F. (ed.) ESA 2001. LNCS, vol. 2161, pp. 170–181. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  18. Ornstein, R.L., Rein, R., Breen, D.L., Macelroy, R.D.: An optimized potential function for the calculation of nucleic acid interaction energies I. Base stacking. Biopolymers 17, 2341–2360 (1978)

    Article  Google Scholar 

  19. Conilione, P.C., Wang, D.: Neural Classification of E.coli Promoters Using Selected DNA Profiles. In: The Fourth IEEE Internation Workshop on Soft Computing and Transdisciplinary Science and Technology. Springer, Heidelberg (2005) (to be published)

    Google Scholar 

  20. Liu, H., Yu, L.: Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans. on Knowledge and Data Engineering (2004)

    Google Scholar 

  21. Kira, K., Rendell, L.A.: The Feature Selection Problem: Traditional Methods and a New Algorithm. In: AAAI, pp. 129–134 (1992)

    Google Scholar 

  22. Zhou, X.J., Dillion, T.S.: A Heuristic - Statistical Feature Selection Criterion For Inductive Machine Learning In The Real World. In: Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988, vol. 1, pp. 548–552 (1988)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Paul C. Conilione & Dianhui Wang

Authors
  1. Paul C. Conilione
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  2. Dianhui Wang
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Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

    Guang-Bin Huang

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© 2005 Springer-Verlag Berlin Heidelberg

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Conilione, P.C., Wang, D. (2005). E-Coli Promoter Recognition Using Neural Networks with Feature Selection. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_7

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  • DOI: https://doi.org/10.1007/11538356_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28227-3

  • Online ISBN: 978-3-540-31907-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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