  Most of the problems in bioinformatics are now the challenges in computing.
This paper aims at building a classifier based on Multiple Attractor Cellular
Automata (MACA) which uses fuzzy logic. It is strengthened with an artificial
Immune System Technique (AIS), Clonal algorithm for identifying a protein
coding and promoter region in a given DNA sequence. The proposed classifier is
named as AIS-INMACA introduces a novel concept to combine CA with artificial
immune system to produce a better classifier which can address major problems
in bioinformatics. This will be the first integrated algorithm which can
predict both promoter and protein coding regions. To obtain good fitness rules
the basic concept of Clonal selection algorithm was used. 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 89.6%. This classifier was tested with 97,000 data
components which were taken from Fickett & Toung, MPromDb, and other sequences
from a renowned medical university. This proposed classifier can handle huge
data sets and can find protein and promoter regions even in mixed and
overlapped DNA sequences. This work also aims at identifying the logicality
between the major problems in bioinformatics and tries to obtaining a common
frame work for addressing major problems in bioinformatics like protein
structure prediction, RNA structure prediction, predicting the splicing pattern
of any primary transcript and analysis of information content in DNA, RNA,
protein sequences and structure. This work will attract more researchers
towards application of CA as a potential pattern classifier to many important
problems in bioinformatics
