Similarity-Based Gene Duplication Prediction in Protein-Protein Interaction Using Deep Artificial Ecosystem Network

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.