Affiliation:
1. Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
Abstract
Background:
Essential proteins play a crucial role in most of the living organisms. The
computer-based task of predicting essential proteins is important for target protein identification, disease
treatment and suitable drug development.
Objective:
Traditionally many experimental and centrality measures have been proposed by researchers to
predict protein essentiality.
Methods:
The prediction accuracy, sensitivity, specificity identified by the traditional methods
is very low.
Results and Discussion:
In this research work, a novel computational based approach such as NCKNN
model has been proposed to identify the essential proteins. The proposed work uses a combination
of network topology measure and machine learning model to predict the essential proteins.
Conclusion:
The proposed work shows a
remarkable improvement than seven traditional centrality based measures such as DC, BC, CC, EC, NC, ECC and SC in
terms of the metrics such as accuracy(A1), precision(P1), recall(R1), sensitivity(SE) and specificity(SP).
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
2 articles.
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