Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development

Preeti, P.; Nath, Swarsat Kaushik; Arambam, Nevidita; Sharma, Trapti; Choudhury, Priyanka Ray; Choudhury, Alakto; Khanna, Vinod Kumar; Strych, Ulrich; Hotez, Peter J.; Bottazzi, María Elena; Rawal, Kamal

doi:10.1007/978-1-0716-3239-0_21

Cited by 1 publication

(1 citation statement)

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“…This approach enabled the filtration of the most discriminative biological and physicochemical properties tailored to each pathogen model. (Preeti et al, 2023)…”

Section: Feature Selectionmentioning

confidence: 99%

Feed Forward Neural Network-Based Approaches for Vaccine Target Identification: Implementation and Insights

Verma,

Garg,

Gandotra

et al. 2024

Preprint

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Vaccine development relies heavily on the identification of appropriate antigen targets to induce robust immune responses. Traditional methods for vaccine target identification often involve laborious experimental procedures and may lack scalability. In recent years, neural network-based approaches have emerged as powerful tools for bioinformatics tasks, including vaccine target identification. In this paper, we present an implementation of neural network-based approaches for vaccine target identification and provide insights into their effectiveness. We utilise a comprehensive dataset of pathogen proteins and employ state-of-the-art Feed-forward neural network architectures for feature learning and prediction. Our results demonstrate the promising performance of neural network models in accurately identifying vaccine targets, outperforming traditional methods in terms of both accuracy and efficiency. Through a detailed analysis, we highlight the key factors influencing model performance and discuss potential avenues for further improvement. Overall, our study underscores the potential of Feed-forward neural network based approaches in vaccine target identification, offering valuable insights for future research in the field.

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“…This approach enabled the filtration of the most discriminative biological and physicochemical properties tailored to each pathogen model. (Preeti et al, 2023)…”

Section: Feature Selectionmentioning

confidence: 99%