Using Genetic Programming to Predict and Optimize Protein Function
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- @Misc{DBLP:journals/corr/abs-2202-04039,
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author = "Iliya Miralavy and Alexander Bricco and
Assaf A. Gilad and Wolfgang Banzhaf",
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title = "Using Genetic Programming to Predict and Optimize
Protein Function",
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howpublished = "arXiv",
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volume = "abs/2202.04039",
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year = "2022",
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month = "23 " # feb,
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keywords = "genetic algorithms, genetic programming",
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URL = "https://arxiv.org/abs/2202.04039",
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eprinttype = "arXiv",
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eprint = "2202.04039",
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timestamp = "Thu, 10 Feb 2022 00:00:00 +0100",
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biburl = "https://dblp.org/rec/journals/corr/abs-2202-04039.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "23 pages",
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abstract = "Protein engineers conventionally use tools such as
Directed Evolution to find new proteins with better
functionalities and traits. More recently,
computational techniques and especially machine
learning approaches have been recruited to assist
Directed Evolution, showing promising results. we
propose POET, a computational Genetic Programming tool
based on evolutionary computation methods to enhance
screening and mutagenesis in Directed Evolution and
help protein engineers to find proteins that have
better functionality. As a proof-of-concept we use
peptides that generate MRI contrast detected by the
Chemical Exchange Saturation Transfer contrast
mechanism. The evolutionary methods used in POET are
described, and the performance of POET in different
epochs of our experiments with Chemical Exchange
Saturation Transfer contrast are studied. Our results
indicate that a computational modeling tool like POET
can help to find peptides with 400 percent better
functionality than used before.",
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notes = "see \cite{Miralavy:2022:PeerJ}",
- }
Genetic Programming entries for
Iliya Miralavy
Alexander R Bricco
Assaf A Gilad
Wolfgang Banzhaf
Citations