生物序列分类:对数据和一般方法的审查
Chunyan Ao1,2,3, Shihu Jiao2, Yansu Wang3
1School of Computer Science and Technology, Xidian University, Xi'an, China.
Research (Washington, D.C.)
|September 17, 2024
概括
机器学习有助于对DNA,RNA和蛋白质功能的生物序列进行分类. 本综述整理了各种分类方法,并提供了一个生物序列数据分析的资源网站.
科学领域:
- 生物技术是生物技术.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 生物序列数据的指数增长需要先进的分析方法.
- 机器学习 (ML) 越来越多地应用于用于预测建模的生物序列分析.
- 生物序列分类对于理解DNA,RNA,蛋白质和的功能和修改至关重要.
研究的目的:
- 审查和组织基于机器学习的生物序列分类方法,重点关注功能和修改.
- 提供一个集中资源网站,提供有关分类方法和数据集的详细信息.
- 为生物序列数据和单细胞测序分析引入有效的模型框架构建.
主要方法:
- 基于机器学习的生物序列分类模型的文献综述.
- 对生物序列的不同分类方法的分类.
- 开发一个支持网站,收集信息和资源.
主要成果:
- 各种生物序列分类模型的全面概述.
- 一个精心策划的网站 (http://lab.malab.cn/~acy/BioseqData/home.html) 提供分类细节和数据集链接.
- 介绍单细胞测序数据分析技术.
结论:
- 机器学习为生物序列分类提供了强大的工具.
- 需要一种结构化的方法和可访问的资源来应对生物序列分析的复杂性.
- 未来的研究应该解决当前的挑战,并探索该领域的新视角.
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