Progress on deep learning in genomics
Received date: 2024-05-27
Revised date: 2024-08-09
Online published: 2024-08-16
Supported by
Natural Science Foundation of Inner Mongolia Autonomous Region(2021ZD05);Sub-theme of the National Dairy Technology Innovation Center Project(2023-JSGG-2);Basic Research Expenses of Universities Directly of Inner Mongolia Autonomous Region(BR221024);Basic Research Expenses of Universities Directly of Inner Mongolia Autonomous Region(BR221113);Funding for Double First-class Construction of Inner Mongolia Agricultural University(BZX202201);Funding for Double First-class Construction of Inner Mongolia Agricultural University(QF202206);Funding for Double First-class Construction of Inner Mongolia Agricultural University(NDYB2022-1)
With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning—an advanced artificial intelligence technology—offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.
Yanchun Bao, Caixia Shi, Chuanqiang Zhang, Mingjuan Gu, Lin Zhu, Zaixia Liu, Le Zhou, Fengying Ma, Risu Na, Wenguang Zhang . Progress on deep learning in genomics[J]. Hereditas(Beijing), 2024 , 46(9) : 701 -715 . DOI: 10.16288/j.yczz.24-151
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