@Article{cmes.2022.019914,
AUTHOR = {G. Naveen Sundar, Stalin Selvaraj, D. Narmadha, K. Martin Sagayam, A. Amir Anton Jone, Ayman A. Aly, Dac-Nhuong Le},
TITLE = {An Intelligent Prediction Model for Target Protein Identification in Hepatic Carcinoma Using Novel Graph Theory and ANN Model},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {133},
YEAR = {2022},
NUMBER = {1},
PAGES = {31--46},
URL = {http://www.techscience.com/CMES/v133n1/48852},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2022.019914}
}