Abstract
Anti-cancer peptides (ACPs) are a promising alternative to traditional chemotherapy. To aid wet-lab and clinical research, there is a growing interest in using machine learning techniques to help identify good ACP candidates computationally. In this paper, we describe DeepACPpred, a novel deep learning model composed of a hybrid CNN-RNN architecture for predicting ACPs. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models.
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Lane, N., Kahanda, I. (2021). DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_7
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