多模式多目标优化与结构性网络控制原则,以优化个性化药物标,用于个别患者的药物发现
Jing Liang1,2, Zhuo Hu1, Ying Bi1
1School of Electrical and Information Engineering, Zhengzhou University, No. 100, Science Avenue, Hightech District, Zhengzhou City 450001, Henan Province, China.
Briefings in bioinformatics
|January 21, 2025
概括
本研究引入了一个新的框架,MMONCP,通过考虑多个网络模块来优化个性化药物目标. 它通过确定早期干预的多式联络药物标来增强癌症治疗.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 网络医学 网络医学
背景情况:
- 个性化药物标 (PDT) 对于优化基于状态转换的患者特定治疗至关重要.
- 现有的方法往往忽视了多式联络药物标 (MDT) 和网络控制中的多目标优化潜力.
研究的目的:
- 为优化个性化药物标提出一个新的多式多目标进化优化框架 (MMONCP).
- 通过纳入多式联络药物目标和多目标优化原则来解决现有方法的局限性.
主要方法:
- 开发了一个受约束的多模式多目标优化问题,具有离散约束和多模式性.
- 设计了一个新的进化算法 (CMMOEA-GLS-WSCD) 具有全球/本地搜索和基于权重的拥挤距离策略.
- 将框架应用于"癌症基因组图谱"中的癌症基因组学数据.
主要成果:
- 与先进的算法相比,MMONCP在算法融合,多样性和MDT识别方面表现出卓越的表现.
- 该框架在曲线得分下实现了更高的面积,这表明预测准确度有所提高.
- 通过分析目标活性和毒性,MMONCP成功检测到早期癌症状态.
结论:
- 在癌症精准医学中,MMONCP提供了一种有效的方法来优化多模式药物标.
- 该框架有助于早期发现疾病状态,并提供及时的治疗建议.
- 这项工作推动了网络控制原则与多目标优化为个性化治疗的整合.
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