关于用于蛋白质家族预测的机器学习技术的全面审查
T Idhaya1, A Suruliandi2, S P Raja3
1Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, TamilNadu, India. idhayathomas003@gmail.com.
The protein journal
|March 1, 2024
概括
这项研究调查了用于预测蛋白质家族的机器学习 (ML),这对于理解蛋白质功能至关重要. 它强调需要新的ML分类器来提高准确性,并解决当前蛋白质组学研究的局限性.
科学领域:
- 蛋白质组学是指蛋白质组学
- 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:
- 蛋白质家族预测对于理解蛋白质功能和进化关系至关重要.
- 现有的方法在准确性,假阳性率和可扩展性方面存在局限性.
- 目前的方法通常依赖于序列或结构同质性,引入偏差.
研究的目的:
- 对机器学习 (ML) 技术进行全面的调查,以进行蛋白质家族预测.
- 探索和确定 ML 方法的改进领域,以完成这一任务.
- 在蛋白质组学中推进蛋白质家族分类的未来研究.
主要方法:
- 对ML的现有文献进行定性和定量分析,以预测蛋白质家族.
- 审查现场应用的各种ML技术和分类器.
- 识别基于ML的蛋白质家族预测研究中的当前趋势和差距.
主要成果:
- 多种ML技术和分类器已被应用于蛋白质家族预测.
- 有相当多的研究利用现有的ML方法,而不是开发新的分类器.
- 显然需要开发新的和改进的ML分类器,以提高预测准确度.
结论:
- 机器学习为蛋白质组学中的蛋白质家族预测提供了强大的工具.
- 该领域需要在开发新的ML分类器方面进行进一步的创新,以克服当前的局限性.
- 改进的蛋白质家族预测将加速药物发现和基因组注释.
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