Leveraging Nucleotide Dependencies for Improved mRNA Vaccine Degradation Prediction

Kadhim Hayawi, Sakib Shahriar, Hany Alashwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

RNA sequence properties prediction is significant for understanding RNA function and its potential applications in medicine and biotechnology. In this study, we developed a novel Gated Recurrent Unit (GRU) deep learning model to predict mRNA vaccine degradation with improved accuracy over traditional machine learning methods and previously reported deep learning approaches. A notable contribution of our approach is the innovative method of feature engineering that accounts for dependencies between nucleotides by shifting the feature values. Our proposed GRU model outperformed XGBoost, Random Forest, and LightGBM models. The GRU Network showed a Mean Columnwise Root Mean Squared Error (MCRMSE) of 0.275 and 0.389 for the public and the private sets, respectively. Despite some limitations, our model provides a strong foundation for future work to refine and expand the capabilities of RNA sequence property prediction. The results of this study have significant implications for RNA research, potentially leading to advancements in understanding RNA function and the development of RNA-targeting therapeutics and diagnostic tools.

Original languageEnglish
Title of host publication2023 20th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2023 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350319439
DOIs
Publication statusPublished - 2023
Event20th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2023 - Giza, Egypt
Duration: Dec 4 2023Dec 7 2023

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference20th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2023
Country/TerritoryEgypt
CityGiza
Period12/4/2312/7/23

Keywords

  • deep learning
  • gated recurrent units (GRU)
  • mRNA vaccine degradation
  • RNA sequence properties
  • vaccine development

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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