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 NC-KNN model
has been proposed to identify the most 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).
Ethylene Receptors, CTRs and EIN2 Target Protein Identification and Quantification Through Parallel Reaction Monitoring During Tomato Fruit Ripening
