Abstract:
In the living organism, almost entire cell functions are performed by protein-protein interactions. As
experimental and computing technology advances, yet more Protein-Protein Interaction (PPI) data becomes
processed, and PPI networks become denser. The traditional methods utilize the network structure to
examine the protein structure. Still, it consumes more time and cost and creates computing complexity
when the system has gene duplications and a complementary interface. This research uses gene expression
patterns to introduce a deep artificial ecosystem for gene duplication counting and cancer cell prediction.
The main objective of this research is to predict the MYC proteins influence level, which is in charge of
controlling cell growth and death in gene expression of lung cancer. Small body parts are responsible for
these protein interactions, which are crucial for understanding life's activities. To achieve the research
objective, a similarity-based clustering approach is employed for gene duplication counting, and Artificial
Ecosystem Optimizer based Minimal Gated Recurrent Unit network (AEOMGRU) network-based
approach is introduced to predict the cancer gene patterns. The proposed models' efficiency is compared to
recently develop bio-inspired optimizer deep neural network techniques such as GAANN, PSOANN, and
classic GRU. The efficiency of the proposed classifier shows the highest concerning the performance
metrics weight average accuracy ratio of 99.08%, average.
