Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution | IEEE Journals & Magazine | IEEE Xplore

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution


Abstract:

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine le...Show More

Abstract:

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 29, Issue: 1, January 2025)
Page(s): 669 - 678
Date of Publication: 18 March 2024

ISSN Information:

PubMed ID: 38498748

Funding Agency:


References

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