AIS-MACA- Z: MACA based Clonal Classifier for Splicing Site, Protein Coding and Promoter Region Identification in Eukaryotes

TL;DR

This paper introduces AIS-MACA-Z, a novel classifier combining fuzzy logic, cellular automata, and immune algorithms to accurately identify splicing sites, protein coding, and promoter regions in eukaryotic DNA sequences.

Contribution

It presents a new MACA-based classifier enhanced with AIS for precise biological region prediction in DNA sequences, handling various lengths with high accuracy.

Findings

Achieves 97% accuracy in splicing site prediction

Provides exact boundaries for protein and promoter regions

Handles DNA sequences of multiple lengths

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 with an artificial immune system technique (AIS), Clonal algorithm for choosing rules of best fitness. The proposed classifier can handle DNA sequences 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 from a renowned medical university.