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dc.contributor.advisorChairperson, Graduate Committee: Indika Kahandaen
dc.contributor.authorLane, Nathaniel Patricken
dc.description.abstractAnticancer 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.en
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.subject.lcshMachine learningen
dc.subject.lcshPredictive analyticsen
dc.subject.lcshNeural networks (Computer science)en
dc.titlePredicting anticancer peptides and protein function with deep learningen
dc.rights.holderCopyright 2020 by Nathaniel Patrick Laneen, Graduate Committee: Brendan Mumey; Diane Bimczok.en

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