您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

From tradition to innovation: conventional and deep learning frameworks in genome annotation

文献类型： 外文期刊

作者： Chen, Zhaojia 1 ; ul Ain, Noor 1 ; Zhao, Qian 3 ; Zhang, Xingtan 1 ;

作者机构： 1.Chinese Acad Agr Sci, Natl Key Lab Trop Crop Breeding, Guangdong Lab Lingnan Modern Agr, Genome Anal Lab,Minist Agr,Agr Genom Inst Shenzhen, Guangzhou 518120, Peoples R China

2.Taiyuan Univ Technol, Coll Biomed Engn, Jinzhong 030600, Peoples R China

3.Fujian Agr & Forestry Univ, State Key Lab Ecol Pest Control Fujian Taiwan Crop, Fuzhou 350002, Peoples R China

4.Fujian Agr & Forestry Univ, Coll Life Sci, Fuzhou 350002, Peoples R China

关键词： genome annotation; genome sequence; bioinformatic; RNA-Seq technology; deep learning

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：6.8； 五年影响因子：7.9 ）

ISSN： 1467-5463

年卷期： 2024 年 25 卷 3 期

页码：

收录情况： SCI

摘要： Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台