机器学习技术用于预测与药物相关的副作用:一个范围审查
Esmaeel Toni1, Haleh Ayatollahi2, Reza Abbaszadeh3
1Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran 14496-14535.
Pharmaceuticals (Basel, Switzerland)
|June 27, 2024
概括
机器学习使用化学和生物特征准确预测药物的副作用. 像Random Forest这样的组合方法在提高药物安全性和开发方面显示出最有希望的结果.
科学领域:
- 药物监督和计算化学
- 药物发现和开发 药物发现和开发
背景情况:
- 准确预测药物的副作用对于患者的安全至关重要.
- 机器学习 (ML) 提供了先进的方法来预测药物不良反应.
- 本综述侧重于利用化学,生物和表型特征的ML方法.
研究的目的:
- 审查用于预测药物相关副作用的机器学习方法.
- 确定药物安全预测中使用的关键特征和算法.
- 评估ML在改善药物开发方面的潜力.
主要方法:
- 覆盖范围审查方法.
- 在多个数据库中进行全面的文献搜索.
- 时间框架:2013年1月1日至2023年12月31日.
主要成果:
- 随机森林,k-最近邻居和支持向量机算法被广泛使用.
- 集合方法,特别是随机森林,强调了整合化学和生物特征的重要性.
- 结合各种特征,显著提高了对药物副作用的预测准确度.
结论:
- 多种功能,数据集和ML算法对于有效的副作用预测至关重要.
- 合奏方法和随机森林表现出卓越的性能.
- 整合化学和生物特征可以提高预测的准确性.
- ML在推动药物开发和临床试验方面具有重大潜力.
- 未来的研究应该探索特定的特征类型,选择方法和基于图形的方法.
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