Open Access
ARTICLE
An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model
G. Naveen Sundar1, Stalin Selvaraj2, D. Narmadha1, K. Martin Sagayam3, A. Amir Anton Jone3, Ayman A. Aly4, Dac-Nhuong Le5,6,*
1
Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
2
Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), School of Chemical & Biotechnology,
SASTRA Deemed to be University, Thanjavur, 613401, India
3
Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
4
Department of Mechanical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia
5
Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
6
Faculty of Information Technology, Duy Tan University, Danang, 550000, Vietnam
* Corresponding Author: Dac-Nhuong Le. Email:
Computer Modeling in Engineering & Sciences 2022, 133(1), 31-46. https://doi.org/10.32604/cmes.2022.019914
Received 23 October 2021; Accepted 25 February 2022; Issue published 18 July 2022
Abstract
Hepatocellular carcinoma (HCC) is one major cause of cancer-related mortality around the world. However, at
advanced stages of HCC, systematic treatment options are currently limited. As a result, new pharmacological
targets must be discovered regularly, and then tailored medicines against HCC must be developed. In this research,
we used biomarkers of HCC to collect the protein interaction network related to HCC. Initially, DC (Degree
Centrality) was employed to assess the importance of each protein. Then an improved Graph Coloring algorithm
was used to rank the target proteins according to the interaction with the primary target protein after assessing
the top ranked proteins related to HCC. Finally, physio-chemical proteins are used to evaluate the outcome of the
top ranked proteins. The proposed graph theory and machine learning techniques have been compared with six
existing methods. In the proposed approach, 16 proteins have been identified as potential therapeutic drug targets
for Hepatic Carcinoma. It is observable that the proposed method gives remarkable performance than the existing
centrality measures in terms of Accuracy, Precision, Recall, Sensitivity, Specificity and F-measure.
Keywords
Cite This Article
Sundar, G. N., Selvaraj, S., Narmadha, D., Sagayam, K. M., Amir, A. et al. (2022). An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model.
CMES-Computer Modeling in Engineering & Sciences, 133(1), 31–46.