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dc.contributor.advisorChairperson, Graduate Committee: Indika Kahandaen
dc.contributor.authorLane, Nathaniel Patricken
dc.date.accessioned2021-02-04T16:25:35Z
dc.date.available2021-02-04T16:25:35Z
dc.date.issued2020en
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/15887en
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.language.isoenen
dc.publisherMontana State University - Bozeman, Norm Asbjornson College of Engineeringen
dc.subject.lcshCanceren
dc.subject.lcshPeptidesen
dc.subject.lcshProteinsen
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.typeThesisen
dc.rights.holderCopyright 2020 by Nathaniel Patrick Laneen
thesis.degree.committeemembersMembers, Graduate Committee: Brendan Mumey; Diane Bimczok.en
thesis.degree.departmentGianforte School of Computing.en
thesis.degree.genreThesisen
thesis.degree.nameMSen
thesis.format.extentfirstpage1en
thesis.format.extentlastpage80en
mus.data.thumbpage60en


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