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An In-depth Evaluation of an Optimized Algorithm for DNA Motive Discovery
International Journal of Systems Science and Applied Mathematics
Volume 3, Issue 4, December 2018, Pages: 67-73
Received: May 29, 2018; Accepted: Jun. 27, 2018; Published: Mar. 7, 2019
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Amannah Constance Izuchukwu, Department of Computer Science, Ignatius Ajuru University of Education, Port Harcourt, Nigeria
Ernest Chukwuka Ukwosah, Department of Computer Science, Federal University, Wukari, Nigeria
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Motive Finding is the process of locating the meaningful patterns in the sequence of DNA, RNA or Proteins. There are many widely used algorithms in practice to solve the motive finding problem and these methods are local search methods. Different search algorithms were discussed which are Gibbs sampling, projection, pattern branching, and profile branching. The limitations surrounding them gave an advantage for the selection of the best algorithm in producing an optimized algorithm for the DNA discovery.
Motive, Optimization, Algorithm, DNA, RNA, Discovery
To cite this article
Amannah Constance Izuchukwu, Ernest Chukwuka Ukwosah, An In-depth Evaluation of an Optimized Algorithm for DNA Motive Discovery, International Journal of Systems Science and Applied Mathematics. Vol. 3, No. 4, 2018, pp. 67-73. doi: 10.11648/j.ijssam.20180304.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Bucher, P. (1990). Weight matrix description for four eukaryotic RNA polymerase II promoter element derived from 502 unrelated promoter sequences. J Mol Biol. 1990; 212: 563–578.
Cliften, P. F., Hillier L. W., Fulton L., Graves T., Miner T., Gish W. R., Waterston R. H., and Johnston M. (2001). Surveying Saccharomyces genomes to Identify functional elements by comparative DNA sequence analysis. Genome Res. 2001; 11: 1175–1186.
Liu V. S., Brutlag D. J., and Liu L. S. (2002). An algorithm for finding protein DNA binding sites with applications to Chromatin-Immunoprecipitation microarray experiments Nat. Biotechnol. 20835-839.
Pradhan L., Medha K. (2008). "Motive Discovery in Biological Sequences". Master's Projects. Paper 106.
Pevzner P. A., Sze S.-H. (2000). Combinatorial approaches to finding subtle signals in DNA sequences, ISBM 2000, 269–278.
Sandelin A, and Wasserman W. W. (2004). Constrained binding site diversity within families of transcription factors enhances pattern discovery Bio informatics J. Mol. Biol. 338207-215.
Tagle D., Koop B., Goodman M., Slightom J., Hess D., and Jones R. (2003). Embryonic ε and γ globin genes of a prosimian primate (Galago crassicaudatus): nucleotide and amino acid sequences, developmental regulation and phylogenetic footprints. J Mol Biol. 1988; 203: 439–455.
Reddy S. U., Arock M., and Reddy A. V. (2010). Planted (l, d) – Motive Finding using Particle Swarm Optimization, JCA Special Issue on “Evolutionary Computation for Optimization Techniques” ECOT, 2010.
Van Helden J., Andre B., and Collado-Vides J. (1998). Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol. 1998; 281: 827–842.
Casati, R., and Smith, B. (1994, February 7). Naive Physics: An Essay in Ontology. Philosophical Psychology, pp. 7-8. Retrieved April 4, 2018.
Cohen, P., and Levesque, H. (1990). Intention is choice with commitment, Artificial Intelligence (Vol. 42). Retrieved March 10, 2018.
Davis, E., and Morgenstern, L. (2004). Introduction: Progress in formal commonsense. New York, U. S. A: Courant Institute, New York University, New York, NY 10012, USA.
Davis, S., and Lessard, A. (2018). The A. I. Revolution Begins With Augumented Intelligence. Signafire.
Gilev, S., Gorban, A., and Mirkes, E. (1991). Small experts and internal conflicts in learning neural networks, 320, (pp. 220-223). Retrieved March 12, 2018.
Gilev, S., Gorban, A., Kochenov, D., Mirkes, Y., Golovenkin, S., Dogadin, S., Shulman, V. (1994). MultiNeuron neural simulator and its medical applications. International Conference On Neural Information Processing, ICONIP1994, 3, (pp. 1261-1264). Retrieved March 13, 2018.
Gorban, A., Mirkes, E., and Tsaregorodtsev, V. (1999). Generation of explicit knowledge from empirical data through pruning of trainable neural Networks. IJCNN’99. International Joint Conference, 6, pp. 4393-4398. Retrieved March 12, 2018.
Haugeland, J. (1978). The nature and plausibility of cognitivism. Behavioural and Brain Sciences (1), (pp. 215–226). Retrieved March 13, 2018.
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