An Optimized Method Implemented In Analyzing of Organ System Using Intelligent Tools


  • Raed Hamed College of Science and Technology, University of Human Development, Sulaimani, Kurdistan Region, Iraq
  • Safa A. Hameed College of Computer Science and Information Technology, University of Anbar, Al-Anbar, Iraq



ANFIS, Confidence value, DNA sequencing


The paper proposes an efficient approach applied in DNA base calling, which concerns efficiency and sensitivity. We utilized the Neuro-Fuzzy model in the analysis issues to determine the confidence value prediction in DNA base calling, that is solved by several attempts applied in the MATLAB tool, the model is implemented for the collected data for each base in the DNA sequencing. The model is designed by using the ANFIS tool, which contains  three subsystems a main system. We obtain three features (peakness, height, and spacing) for each base from the three subsystems and in the main system use these three features as the input to predict the confidence value for each base in the DNA. This achieves a high accuracy in the obtained results with high-performance.


Basic format for books:

[1] T. A. Hiwarkar1, R. S. Iyer2, "New Applications of Soft Computing, Artificial Intelligence, Fuzzy Logic & Genetic Algorithm in Bioinformatics", International Journal of Computer Science and Mobile Computing, 2013.
[2] H. Ressom, P. Natarjan, R.S. Varghese, M. T. Musavi, "Applications of fuzzy logic in genomics", Journal of Fuzzy Sets and Systems 152, 125–138, 2005.
[3] R. I.Hamed, S.I. Ahson, "Confidence value prediction of DNA sequencing with Petri net model", Journal of King Saud University – Computer and Information Sciences , 23, 79–89, 2011.
[4] L.A. Zadeh, "Fuzzy logic, neural networks and soft computing", Comm. ACM 37 (3) 77–84, 1994.
[5] J. L. Pálsdóttir, " NEURO-FUZZY CONTROL ", Soft Computing, 2005.
[6] X. Ji, W. Wang, "A Neural Fuzzy System for Vibration Control in Flexible Structures", Intelligent Control and Automation, 2, 258-266, 2011.
[7] H. N. Teodorescu, "Genetics, Gene Prediction, and Neuro-Fuzzy Systems", the Context and A Program Proposal, F.S.A.I., Vol. 9, Nos. 1–3, pp. 15–22.
[8] D. Neagu, V. Palade, "A neuro-fuzzy approach for functional genomics data interpretation and analysis Neural Comput & Applic", 12: 153–159, 2003.
[9] J. B. Golden, D. Torgersen, and C., " Pattern recognition for automated DNA sequencing: I. On-line signal conditioning and feature extraction for basecalling". In Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (ed. L. Hunter, D. Searls, and J. Shavlick), pp. 136–144. AAAI Press, Menlo, Park, CA, 1993.
[10] A.J. Berno, "A graph theoretic approach to the analysis of DNA sequencing data", Genome Res. 6: 80–91, 1996.
[11] B. Ewing and P. Green, "Base-calling of automated sequencer traces using phred: II. Error probabilities", Genome Research, 8, 186-194, 1998.
[12] D. Thornley, S. Petridis, " Decoding Trace Peak Behaviour A Neuro-Fuzzy Approach ", 2007.
[13] S. A. Hameed, R. I. Hamed, " Expert System of Fuzzy Logic Reasoning Based Implementation for DNA base calling ", International journal of business and ict, Vol.2, No.3-4, 2016.