TOXIFY: a deep learning approach to classify animal venom proteins
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.
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
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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