Leveraging Nucleotide Dependencies for Improved mRNA Vaccine Degradation Prediction

Document Type

Conference Proceeding

Source of Publication

2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

Publication Date

12-7-2023

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.

ISBN

979-8-3503-1943-9

Publisher

IEEE

Volume

00

First Page

1

Last Page

6

Disciplines

Computer Sciences | Life Sciences

Keywords

mRNA vaccine degradation, RNA sequence properties prediction, GRU Network, deep learning model, nucleotide dependencies

Indexed in Scopus

no

Open Access

no

Link to Full Text

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