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Perspectives and applications of machine learning for evolutionary developmental biology

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Abstract

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, “omic” studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

Graphical abstract: Perspectives and applications of machine learning for evolutionary developmental biology

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Publication details

The article was received on 15 May 2018, accepted on 10 Aug 2018 and first published on 13 Aug 2018


Article type: Review Article
DOI: 10.1039/C8MO00111A
Citation: Mol. Omics, 2018, Advance Article
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    Perspectives and applications of machine learning for evolutionary developmental biology

    B. C. Feltes, B. I. Grisci, J. D. F. Poloni and M. Dorn, Mol. Omics, 2018, Advance Article , DOI: 10.1039/C8MO00111A

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