类分类格局:关于类型,数据库,数据集,预测器架构和性能的深入系统文献综述
Muhammad Nabeel Asim1, Tayyaba Asif2, Faiza Mehmood3
1German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany; Intelligentx GmbH (intelligentx.com), Kaiserslautern, Germany.
Computers in biology and medicine
|February 23, 2025
概括
本综述整合了用于类别的人工智能 (AI) 的信息,详细介绍了22种类型,288个数据集和230个AI应用程序,以指导未来的研究和开发.
科学领域:
- 生物化学和生物信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 基于的疗法在制药领域越来越重要,用于治疗癌症和糖尿病等疾病.
- 准确的类分类对于开发特定应用至关重要,但当前的人工智能工具有限.
- 对于22种类型,有超过230个人工智能 (AI) 应用程序,但仍有显著的开发潜力.
研究的目的:
- 为人工智能驱动的类分类提供全面的审查和整合平台.
- 将22种不同的类型分为四个主要类别:调节性,治疗性,营养性和传递性.
- 通过总结现有的资源和应用来解决人工智能驱动的类别的差距.
主要方法:
- 对用于类分类基准数据集的47个数据库的审查.
- 288个基准数据集的摘要,用于人工智能驱动的类别.
- 详细分析了230个AI应用中使用的197个序列表示学习方法和94个分类器.
主要成果:
- 22种类型的分类,分为调控性,治疗性,营养性和输送性等级.
- 汇编了288个基准数据集和230个人工智能驱动的类别应用程序.
- 对22种类型和288个数据集的230个AI应用程序的性能评估.
结论:
- 本综述整合了有关人工智能驱动的类分类的分散信息,为研究人员创造了一个有价值的资源.
- 这些发现突出了人工智能在类分类中的现状,并确定了未来发展的领域.
- 合并的平台将加速开发新的AI驱动的类分类应用程序.
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