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    TOXIFY: a deep learning approach to classify animal venom proteins

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    Author
    Cole, T. Jeffrey; Brewer, Michael S.
    Abstract
    In 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.
    URI
    http://hdl.handle.net/10342/8341
    Subject
    Venom, Deep learning, Protein classification, Transcriptome, Proteome
    Date
    2019-06-28
    Citation:
    APA:
    Cole, T. Jeffrey, & Brewer, Michael S.. (June 2019). TOXIFY: a deep learning approach to classify animal venom proteins. , (), - . Retrieved from http://hdl.handle.net/10342/8341

    Display/Hide MLA, Chicago and APA citation formats.

    MLA:
    Cole, T. Jeffrey, and Brewer, Michael S.. "TOXIFY: a deep learning approach to classify animal venom proteins". . . (), June 2019. October 03, 2023. http://hdl.handle.net/10342/8341.
    Chicago:
    Cole, T. Jeffrey and Brewer, Michael S., "TOXIFY: a deep learning approach to classify animal venom proteins," , no. (June 2019), http://hdl.handle.net/10342/8341 (accessed October 03, 2023).
    AMA:
    Cole, T. Jeffrey, Brewer, Michael S.. TOXIFY: a deep learning approach to classify animal venom proteins. . June 2019; (): . http://hdl.handle.net/10342/8341. Accessed October 03, 2023.
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