Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches

Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches

Predicting mRNA vaccine degradation rates with precision is essential for ensuring stability, efficacy, and optimal deployment strategies, particularly given the unique challenges posed by their rapid degradation. This study introduces a comprehensive approach that integrates bioinformatic insights...

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Bibliographic Details
Main Authors: Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Deep neural network
degradation rate
dropout
hyperparameter optimization
interdisciplinary
mRNA vaccines
Online Access:https://ieeexplore.ieee.org/document/11107401/
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