基于机器学习的方法的实证比较和分析,用于药用蛋白质的识别
Watshara Shoombuatong1, Nalini Schaduangrat1, Jaru Nikom2
1Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700.
EXCLI journal
|October 2, 2023
概括
这项研究回顾了用于预测可用药蛋白的计算方法,这对于药物发现至关重要. 它评估了机器学习方法,突出了它们的优缺点,以指导未来开发准确的预测器.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 识别药物点对于药物开发至关重要,但实验方法耗时.
- 计算方法,特别是机器学习 (ML),加速了从初级序列中预测可用药物的蛋白质.
研究的目的:
- 提供首次对最先进的计算方法进行全面的调查,以在体中预测可用药的蛋白质.
- 在多个方面评估现有计算方法的优缺点.
主要方法:
- 审查了用于药用蛋白质预测的现有基准数据集和机器学习 (ML) 方法.
- 在不同的基准数据集中调查计算方法的有效性.
- 总结了关键功能,现有的网络服务器/软件,并确定了当前的限制.
主要成果:
- 用于药物可用蛋白质预测的计算方法在数据集,特征,ML算法和评估策略中表现出多样性.
- 不同基准数据集的有效性各不相同,需要仔细选择方法.
- 确定了关键特征和现有工具,以及当前的局限性.
结论:
- 本综述提供了对计算可用药蛋白预测方法的全面评估.
- 为开发更准确,更有效的预测模型提供指导.
- 旨在推进在体中药物标识领域的发展.
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