Linear Prediction of Nucleotides in a Genome Sequence
Venkateswarlu Pedakolmi1, E. G. Rajan2
1Venkateswarlu Pedakolmi*, Research Scholar, Dept. of Computer Science, MG-NIRSA, Affiliated to University of Mysore, Manasagangotri, Mysore, Karnataka, India.
2E. G. Rajan, Director, MG-NIRSA; Director, PRC Global Technologies Inc., Ontario, Canada.
Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4142-4146 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27671081219/2019©BEIESP | DOI: 10.35940/ijitee.L2767.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nucleotides are organic molecules, which are monomer units that form polymers of nucleic acid „deoxyribonucleic acid (DNA)‟ and „ribonucleic acid (RNA)‟. The four nucleotides A, T, G and C get connected by phosphodiester bonds to form strands. Strand formation depends on innumerable factors related to inter and intra cellular parameters and functions. One cannot precisely say that a particular strand gets formed using such and such rules. The infinite possibilities of strand formation cannot be determined experimentally or in the framework of classical genetics. One can alternatively formulate a notion of the “Language of Genomes” wherein one can finitely specify infinite strands. This paper introduces a novel prediction algorithm, which generates possible strands based on available nucleotides statistics.
Keywords: Linear Prediction, Genome Sequences.
Scope of the Article: Regression and Prediction