Title:Robust Computational Model for Diagnosis of Mitogenic Activated Protein
Kinase Leading to Neurodegenerative Diseases
Volume: 18
Issue: 1
Author(s): Shruti Jain and Ayodeji Olalekan Salau*
Affiliation:
- Department of Electrical/Electronics and Computer Engineering, Afe Babalola University,
Ado-Ekiti, Nigeria
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai,
India
Keywords:
MAPK, attributes, machine learning, convolution neural network, accuracy, neurodegenerative disease.
Abstract:
Background: Computational modeling is used to develop solutions by formulating and
modeling real-world problems. This research article presents an innovative approach to using a
computational model, as well as an evaluation of software interfaces for usability.
Methods: In this work, a machine learning technique is used to classify different mitogenic activated
protein kinases (MAPK), namely extracellular signal-regulated kinase (ERK), c-Jun amino (N)-
terminal kinases (JNK), and mitogenic kinase (MK2) proteins. A deficiency of ERK and JNK leads
to neurodegenerative diseases, such as Parkinson's disease, Alzheimer's disease (AD), and prion
diseases, while the deficiency of MK2 leads to atherosclerosis. In this study, images from a heat
map were normalized, scaled, smoothed, and sharpened. Different feature extraction methods have
been used for various attributes, while principal component analysis was used as a feature selection
technique. These features were extracted with machine learning algorithms to produce promising
results for clinical applications.
Results: The results show that ANN achieves 97.09%, 96.82%, and 96.01% accuracy for JNK,
ERK, and MK2 proteins, respectively, whereas CNN achieves 97.60%, 97.36%, and 96.81% accuracy
for the same proteins. When CNN is used, the best results are obtained for JNK protein, with a
training accuracy of 97.06% and a testing accuracy of 97.6%.
Conclusion: The proposed computational model is validated using a convolution neural network
(CNN). The effect of the hidden layer on different activation functions has been then observed using
ANN and CNN. The proposed model may assist in the detection of various MAPK proteins,
yielding promising results for clinical diagnostic applications.
