Comments: 6,1-6 Pages, Journal of Artificial Intelligence Research

Computer Science > Computational Engineering, Finance, and Science Title: mercurial AIS-MACA- Z: MACA based Clonal Classifier mercurial for Splicing Site, Protein Coding mercurial and Promoter Region Identification in Eukaryotes
(Submitted on 4 Apr 2014) Abstract: Bioinformatics incorporates information regarding biological data storage, accessing mechanisms and presentation of characteristics within this data. Most of the problems in bioinformatics and be addressed efficiently by computer techniques. This paper aims at building a classifier based on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic with version Z to predict splicing site, protein coding and promoter region identification in eukaryotes. It is strengthened mercurial with an artificial immune system technique (AIS), Clonal algorithm for choosing rules of best fitness. The proposed classifier can handle DNA sequences mercurial of lengths 54,108,162,252,354. This classifier gives the exact boundaries of both protein and promoter regions with an average accuracy of 90.6%. This classifier can predict the splicing site with 97% accuracy. This classifier was tested with 1, 97,000 data components which were taken from Fickett & Toung , EPDnew, and other sequences mercurial from a renowned medical university.
Comments: 6,1-6 Pages, Journal of Artificial Intelligence Research & Advances Volume 1, Issue 1,2014. arXiv admin note: text overlap with arXiv:1403.5933 , arXiv:1404.0453 Subjects: Computational Engineering, Finance, and Science (cs.CE) ; Learning (cs.LG) Cite as: arXiv:1404.1144 [cs.CE]   (or arXiv:1404.1144v1 [cs.CE] for this version)
Link back to: arXiv , form interface , contact .