More-SPEED: Enhancing Protein Activity Prediction from DNA Sequences

Abstract

This work presents More-SPEED, a novel model for accurately predicting protein activity while minimizing computational demands. Leveraging optimized structures and data preprocessing techniques, More-SPEED achieves high accuracy in protein activity prediction. The model incorporates the data compression three dimension (DC-3D) layer, utilizing the graph mining pattern-fist frequency graph mining (GMP-FFGM) algorithm for efficient preprocessing of complex Deoxyribonucleic acid (DNA) sequence datasets. Additionally, the deterministic structure network using the natural-inspired optimization algorithm called Whale Optimization Algorithm (DSN-WOA) structure optimizes parameters of the Biological dynamic long short term memory (BDLSTM) model, reducing processing time and eliminating manual parameter selection. The BDLSTM layer plays a crucial role in matching codons and predicting protein names, reducing computational complexity without compromising accuracy. The Bi-Rule layer efficiently determines protein activity, especially in disease contexts, providing valuable insights in a shorter time compared to alternative approaches. Evaluation metrics validate the effectiveness of More-SPEED in accurately predicting protein activity, making it a promising solution for advancing protein research.