Predicting anticancer peptides and protein function with deep learning
Lane, Nathaniel Patrick
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Anticancer 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 work, we develop DeepACPpred, a novel deep learning model for predicting ACPs using their amino acid sequences. Using several gold-standard ACP datasets, we demonstrate that DeepACPpred is highly effective compared to state-of-the-art ACP prediction models. Furthermore, we adapt the above neural network model for predicting protein function and report our experience with participating in a community-wide large-scale assessment of protein functional annotation tools.