Literature DB >> 30168572

Perspectives and applications of machine learning for evolutionary developmental biology.

Bruno César Feltes1, Bruno Iochins Grisci, Joice de Faria Poloni, Márcio Dorn.   

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.

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Year:  2018        PMID: 30168572     DOI: 10.1039/c8mo00111a

Source DB:  PubMed          Journal:  Mol Omics        ISSN: 2515-4184


  1 in total

1.  Developmental Physiology: Grand Challenges.

Authors:  Warren Burggren
Journal:  Front Physiol       Date:  2021-06-10       Impact factor: 4.566

  1 in total

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