1Professor, Department of CSE, Shri Vishnu Engineering College for Women, Bhimavaram, India
2Assistant Professor, Department of CSE, University College of Engineering, JNTU Kakinada, India
Corresponding author details:
Pokkuluri Kiran Sree, Professor
Dept of CSE
Shri Vishnu Engineering College for Women
© 2020 Sree PK, et al. This is
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Most of the grand challenges in Bioinformatics can be addressed my Deep Learning and Cellular Automata . We have developed various classifiers to address essential problems in Bioinformatics. After that, we have done a literature survey, which indicates that many computer algorithms are used to solve open issues like Protein Coding Region (PCR), Promoter prediction (PR), and Chronic Disease Prediction (CDP) individually [2,3]. After thorough research, we conclude that many of the chronic diseases, homology searches, gene finding, and genomic sequence finding could be addressed quickly with a combination of deep learning augmented with cellular automata [4-6].
We understood when a deep learning model is augmented with non-linear cellular automata, and we can have the following advantages.
Deep learning ; Bioinformatics ; Cellular automata ; Non-linear rules
PCR: Protein Coding Regions
CDP: Chronic Disease Prediction
NLCA: None Linear Cellular Automata
CA: Cellular Automata
DNN: Deep Neural Network CNN:
Convolution Neural Network NL-MACA:
Non-Linear Multiple Attractor Cellular Automata
Based on the above intuition, the design applicable for various problems was given below in (Figure1). This system design is vital in addressing these problems as the input can be any sequence. Hybrid non-linear CA  rules are used to process the data either in the number of three or six or nine.
Figure 1: Design of a common platform to address various problems in Bioinformatics
PCR  prediction process the DNA sequence in terms of three as Codons are identified
in three in number, and so on. A deep learning technique DNN (Deep Neural Network) 
is used to classify the sequence into many classes from which once classes are predicted by
NL-MACA (Non-Linear Multiple Attractor Cellular Automata). The classes or the prediction
analysis can be the result of the entire design.
We have done a thorough literature survey to find a common
platform to address various problems in Bioinformatics and are
successful in this regard. The strength of the classifier lies in the
adaptability, less overhead, more sensitivity, specificity, more
accuracy, less computing time, and processing large datasets .
Complex problems like secondary, quaternary protein structure
prediction can be carried within 0.7 Nanoseconds.
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