Using Genetic Programming to Predict and Optimize Protein Function

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@Misc{DBLP:journals/corr/abs-2202-04039,

• author = "Iliya Miralavy and Alexander Bricco and Assaf A. Gilad and Wolfgang Banzhaf",
• title = "Using Genetic Programming to Predict and Optimize Protein Function",
• howpublished = "arXiv",
• volume = "abs/2202.04039",
• year = "2022",
• month = "23 " # feb,
• keywords = "genetic algorithms, genetic programming",
• URL = "https://arxiv.org/abs/2202.04039",
• eprinttype = "arXiv",
• eprint = "2202.04039",
• timestamp = "Thu, 10 Feb 2022 00:00:00 +0100",
• biburl = "https://dblp.org/rec/journals/corr/abs-2202-04039.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "23 pages",
• 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.",
• notes = "see \cite{Miralavy:2022:PeerJ}",

}

Genetic Programming entries for Iliya Miralavy Alexander R Bricco Assaf A Gilad Wolfgang Banzhaf

Citations