A Computationally Intelligent System for Prediction of Protein Function using Pattern Recognition
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Date
2020
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UMT,Lahore
Abstract
Pattern recognition systems are emerging in a multitude of computer applications. They have been part of many computationally intelligent systems like optical character recognition systems, biometric verification systems, weather forecasting, decision support systems, etc. Computationally intelligent systems are also considered significant components in the toolkit of a biologist. Such systems are essentially required by the majority of the modern research projects in the biological sciences. Most of these projects use computationally intelligent systems for either DNA or protein sequence analysis. Protein molecules are composed of a large sequence of Amino Acids. With the rapid discovery of new protein sequences in past decades, functional identification of the hypothetical or uncharacterized protein sequence or its primary structure is confronted as a challenging task in computational biology and proteomics. To explore the problems associated with protein function prediction, some computational techniques were proposed in the past, but are still not effective in terms of efficiency and accuracy. Based on pattern recognition feature extractions and machine learning classification algorithms, the outcome of the current research study has developed a computationally intelligent system that will be an effective practical approach in predicting protein functional attributes. The observed results, obtained from the proposed system in predicting protein functions, have shown better performance outcomes in terms of accuracy as compared to the existing state-of-art systems. Finally, we conclude that the proposed system will be effective and useful in problems relating to Bioinformatics, medicinal biology and drug discovery. This system will enhance the experimental research dynamics into exceptionally interpreting and analyzing the biological data for quick progress in areas of vaccine discovery, drug interactions, disease predictions and most importantly biological datasets characterizations.