Abstract
In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a hybrid approach based on combining feature selection (FS), fuzzy weighted pre-processing, and C4.5 decision tree classifier (DCS). Dimensionality of E.coli Promoter Gene Sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed approach consists of three stages. Firstly, we have used the FS process to reduce the dimensionality of E.coli Promoter Gene Sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. Secondly, fuzzy weighted pre-processing has been used to weight E.coli Promoter Gene Sequences dataset that has 4 attributes in interval of [0,1]. Finally, C4.5 decision tree classifier algorithm has been run to estimation the E.coli Promoter Gene Sequences. In order to show the performance of the proposed system, we have used the predicton accuracy and 10-fold cross validation. 93.33% classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in the prediction of E.coli Promoter Gene Sequences.
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References
Harley, C., Reynolds, R.: Analysis of E.coli promoters sequences. Nucl. Acids Res. 15, 2343–2361 (1987)
Geoffrey, G.T., Jude, W.S., Michiel, O.N.: Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks. In: Proc. of the eight national conf. on Artificial Intelligence, pp. 861–866 (1990)
UCI Machine Learning Repository (last arrived: January, 2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Polat, K., Şahan, S., Kodaz, H., Günes, S.: A New Classification Method for Breast Cancer Diagnosis: Feature Selection Artificial Immune Recognition System. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 830–838. Springer, Heidelberg (2005)
Polat, K., Şahan, S., Güneş, S.: A New Method to Medical Diagnosis: Artificial Immune Recognition System (AIRS) with Fuzzy Weighted Pre-processing and Application to ECG Arrhythmia. Expert Systems with Applications 31(2), 264–269 (2006)
Mitchell, M.T.: Machine Learning. McGraw-Hill, Singapore (1997)
Quinlan, J.R.: Induction of decision trees, Machine Learning, vol. 1, pp. 81–106 (1986)
Ranawana, R., Palade, V.: A Neural Network Based Multi-Classifier System for Gene Identification in DNA Sequences. Neural Computing and Applications 14(2), 122–131 (2005)
Mahadevan, I., Ghosh, I.: Analysis of E.coli promoter structures using neural networks. Nucleic Acids. Res. 22(11), 2158–2165 (1994)
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Akdemir, B., Polat, K., Güneş, S. (2007). Prediction of E.Coli Promoter Gene Sequences Using a Hybrid Combination Based on Feature Selection, Fuzzy Weighted Pre-processing, and Decision Tree Classifier. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_16
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DOI: https://doi.org/10.1007/978-3-540-74819-9_16
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