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Updated: Jun 13, 2025

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一种基于深度学习的方法,用于预测药物副作用的频率类,该方法基于多源相似性融合
Haochen Zhao1, Dingxi Li1, Jian Zhong1
1Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|May 27, 2025
概括
我们开发了一种新的多源相似性融合 (MSSF) 模型,以准确预测药物副作用频率. 它的性能优于现有的方法,为药物开发和患者安全提供了一个有前途的工具.
科学领域:
- 药物监督 药物监督 药物监督
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物的副作用是与治疗目标无关的不良反应.
- 准确预测药物的副作用频率对于安全的药物使用和药物开发至关重要.
- 现有的用于药物副作用预测的计算方法经常遭受过度匹配和边界问题.
研究的目的:
- 开发一种先进的计算模型,用于预测药物副作用的频率类.
- 克服现有的回归模型在处理频率类和过度装配方面的局限性.
- 引入一种新的方法,利用多源相似性融合和贝叶斯变异推理.
主要方法:
- 开发了一个多源相似性融合 (MSSF) 模型.
- 包括一个多源功能融合模块和一个自我注意机制.
- 利用贝叶斯变异推理来准确预测药物副作用频率等级.
主要成果:
- 在各种评估设置中,MSSF在现有模型中表现出优越的性能.
- 在交叉验证,冷启动实验和独立测试中观察到一致的结果.
- 视觉分析和案例研究证实了MSSF强大的特征提取和预测能力.
结论:
- 该MSSF模型在预测药物副作用频率方面取得了重大进展.
- 它深入探索药物副作用关系的能力提高了预测准确度.
- 在提高药物安全性和为临床实践提供信息方面,MSSF显示出相当大的前景.
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