DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. Lane, N. & Kahanda, I. In Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M. S., & Casado-Vara, R., editors, Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020), pages 60–69, Cham, 2021. Springer International Publishing. abstract bibtex 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.
@InProceedings{10.1007/978-3-030-54568-0_7,
author = {Lane, Nathaniel and Kahanda, Indika},
booktitle = {Practical Applications of Computational Biology {\&} Bioinformatics, 14th International Conference (PACBB 2020)},
title = {DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides},
editor = {Panuccio, Gabriella and Rocha, Miguel and Fdez-Riverola, Florentino and Mohamad, Mohd Saberi and Casado-Vara, Roberto},
isbn = {978-3-030-54568-0},
pages = {60--69},
publisher = {Springer International Publishing},
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.},
address = {Cham},
year = {2021},
}
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