AUTHOR(S):

Hanan Hendy, Wael Khalifa, Mohamed Roushdy, Abdel-Badeeh M. Salem

TITLE

The Usage of Machine Learning Paradigms on Protein Secondary Structure Prediction

KEYWORDS

Case Based Reasoning, Decision Trees, Bioinformatics, Machine Learning, Protein Secondary Structure Prediction

ABSTRACT

The significance of the secondary structure prediction process is something no one can deny. This is because of the importance of protein in all our human system functionalities. Protein forms every single element in the body using its amino acids. These amino acids start to bond together forming other protein structures. A lot of diseases can be diagnosed by simply checking the deformation of these structures. The problem is that it takes a lot of effort to get from the primary protein structure –aka amino sequence– to the secondary, tertiary and quaternary structures it forms. Through the past decade a lot of machine learning methods arose that predicted the secondary structure and then predicted the tertiary from it. Most of these methods were based on Neural Networks paradigm only. This paper aims to show how other machine learning techniques have been used to predict the secondary structure. The techniques used are; Case Based Reasoning, Bayes Network, Decision Tables and Decision trees. The highest accuracy reached was when using Bayes network to predict Beta secondary structure only, it reached an accuracy of 75.89 %.

Cite this paper

Hanan Hendy, Wael Khalifa, Mohamed Roushdy, Abdel-Badeeh M. Salem. (2016) The Usage of Machine Learning Paradigms on Protein Secondary Structure Prediction. International Journal of Circuits and Electronics, 1, 72-77