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TOXIFY: a deep learning approach to classify animal venom proteins

dc.contributor.authorCole, T. Jeffrey
dc.contributor.authorBrewer, Michael S.
dc.date.accessioned2020-04-23T18:32:19Z
dc.date.available2020-04-23T18:32:19Z
dc.date.issued2019-06-28
dc.description.abstractIn the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins.en_US
dc.identifier.doi10.7717/peerj.7200
dc.identifier.urihttp://hdl.handle.net/10342/8341
dc.subjectVenom, Deep learning, Protein classification, Transcriptome, Proteomeen_US
dc.titleTOXIFY: a deep learning approach to classify animal venom proteinsen_US
dc.typeArticleen_US
ecu.journal.namePeerJen_US
ecu.journal.pagese7200en_US
ecu.journal.volume7en_US

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