Perspectives and applications of machine learning for evolutionary developmental biology

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.

Developmental Physiology: Grand Challenges

“…Modified from Mueller et al (2016). risk of human-created error (Feltes et al, 2018;Villoutreix, 2021).…”

“…These and other emerging techniques typically produce vast amounts of data, often in the form of complex images. Not surprisingly, then, developmental biology, including developmental physiology, has begun to exploit to machine (“deep”) learning, which can analyze large data sets without direct human involvement and the associated risk of human-created error (Feltes et al, 2018 ; Villoutreix, 2021 ).…”

An Optimal Feature Parameter Set Based on Gated Recurrent Unit Recurrent Neural Networks for Speech Segment Detection

“…Convolution, activation or using a rectified linear unit (ReLU), and pooling represent the three most frequent layers. The above-mentioned operations are performed repeatedly over tens or hundreds of layers, with every layer learning for the purpose of detecting various features in the input data [53]. Figure 8 shows the general framework of convolution neural networks (CNNs).…”

Cyberbiosecurity: A Call for Cooperation in a New Threat Landscape

“…Industry interest in artificial intelligence (AI) has experienced a resurgence in recent years due to increased computing power, advancing applications of neural networks, and an emergence of new machine and deep learning techniques across the biology sector. Biotechnology companies are successfully utilizing these developments for drug design and development (Zilinskas, 2017), genomics (Pauwels and Vidyarthi, 2017), evolutionary biology (Feltes et al, 2018), protein folding (Paladino et al, 2017), and more. This rapid and evolving interest in the landscape of new AI technologies has led to emerging threat domains related to information privacy and storage, ownership over biological and genetic data, and applications of powerful technologies (Pauwels, 2018).…”