Perspectives and applications of machine learning for evolutionary developmental biology

摘要

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand howdevelopment was modulated by the evolutionary process. In this sense, ‘‘omic’’ studies emerged as a powerfulally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools becomenecessary to analyze the growing amount of information. Among computational approaches, machine learningstands 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 anddevelopment. We draw clear perspectives and argue how evolution impacted machine learning techniques.