Abstract
This work presents More-SPEED, a novel model for accurately predicting protein activity while minimizing computational demands. Leveraging optimized structures anddata preprocessing techniques, More-SPEED achieves high accuracy in protein activity prediction. The model incorporates thedata compression three dimension (DC-3D)layer, utilizing the graph mining pattern-fist frequency graph mining (GMP-FFGM)algorithmfor efficient preprocessing of complex Deoxyribonucleic acid (DNA)sequence datasets. Additionally, the deterministic structure network using the natural-inspired optimization algorithm calledWhale 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 effectivenessof More-SPEED in accurately predicting protein activity, making it a promising solution for advancing protein research
Recommended Citation
Janabi, Samahe Al and Kadhuim, Zena
(2023)
"More-SPEED: Enhancing Protein Activity Prediction from DNA Sequences,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
4, Article 5.
DOI: https://doi.org/10.52866/ijcsm.2023.04.04.005
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss4/5
