Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations

Muhammad Alsherbiny, Ibrahim Radwan, Nour Moustafa, Deep Bhuyan, Muath El-Waisi, Dennis Chang, Chun Guang Li

Research output: Contribution to journalArticlepeer-review

Abstract

The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations warrants the consideration of different synergy metrics to develop efficient predictive models. Furthermore, neglecting combination sensitivity may lead to biased synergistic combinations, which are ineffective in cancer treatment. In this paper, we propose a deep learning-based model, SynPredict, which effectively predicts synergy in five synergy metrics together with the combination sensitivity score. SynPredict assesses the impact of multimodal fusion architectures of the input data, including the gene expression data of cancer cells, along with the representative chemical features of drugs in pairwise combinations. Both ONEIL and ALMANAC anticancer combination datasets are employed comparatively. The impact of the training datasets was more significant and consistent across most synergy models than input data fusion architectures. Synpredict outperforms the state-of-the-art predictive models, including DeepSynergy, AuDNN synergy, TranSynergy and DrugComb, with up to 74% decline in the mean square error. We highlight the pivotal need to consider a multiplex of synergy metrics and the combined sensitivity in the predictive models.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
Publication statusE-pub ahead of print - 9 Nov 2021

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