| Literature DB >> 30168572 |
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.Entities:
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Year: 2018 PMID: 30168572 DOI: 10.1039/c8mo00111a
Source DB: PubMed Journal: Mol Omics ISSN: 2515-4184