Artificial Neural Networks and their Use in Agricultural Genetic Engineering and Protein Estimation

Amit Kotiyal

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Aashish Dudi *

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

M. Lakshmi

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Adesh Gaurav

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Bikram Shah

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Ritam Mondal

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Lalit Kadu

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

Nitin Kalyan

School of Agriculture, Lovely Professional University, Phagwara-144411, Punjab, India.

*Author to whom correspondence should be addressed.


Abstract

A mathematical model called an artificial neural network uses certain algorithms to anticipate and predict various events. This model has multiple layers, including input, hidden, and output layers. By altering its algorithms, various outputs can be produced based on the input utilized. Biological neuron mechanization has an impact on artificial neural networks. As biological neurons have a propensity to learn and train sets of data for producing biased outputs by spotting and removing variances in them, ANN also works on these principles. Although this model has many uses, it has historically been employed in biological experiments using the supervised learning method, one of which is to predict protein secondary structure. This allows one to identify the positions of different amino acids that are ordered complicated protein structures, which are very appealing in genetic engineering. By this model scientists can map out and isolate a desirable gene in genetic material without going through intense laborious experiments. This report summarizes all the objectives linked to artificial neural networks as well as their applications in bioengineering by examining many related studies.

Keywords: Bioengineering, artificial intelligence, protein analysis, bioinformatics, neural network


How to Cite

Kotiyal , Amit, Aashish Dudi, M. Lakshmi, Adesh Gaurav, Bikram Shah, Ritam Mondal, Lalit Kadu, and Nitin Kalyan. 2023. “Artificial Neural Networks and Their Use in Agricultural Genetic Engineering and Protein Estimation”. International Journal of Environment and Climate Change 13 (8):95-108. https://doi.org/10.9734/ijecc/2023/v13i81936.