使用进化计算的多组学识别分子和模块生物标记物,用于早期诊断和治疗复杂疾病
Han Cheng1, Mengyu Liang1, Yiwen Gao1
1School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China.
Genes
|March 28, 2025
概括
识别疾病生物标志物 (DBs) 对个性化医疗至关重要. 本研究回顾了进化计算 (EC) 方法,用于分析复杂的多组数据,以改善高精度生物标志物发现.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 个性化医疗是个性化的医疗.
背景情况:
- 准确的疾病生物标志物 (DB) 识别对于个性化医学的早期诊断和治疗至关重要.
- 当前的多态数据分析方法与复杂的动态作斗争,限制了高精度生物标志物表征.
- 现有的智能技术在处理用于生物标志物发现的高维奥米克数据方面面临着挑战.
研究的目的:
- 审查和探索用于识别疾病生物标志物的进化计算 (EC) 方法.
- 为了解决复杂的动态特征固有的多态数据.
- 加强EC理论在生物信息学中的应用,用于生物标记物识别.
主要方法:
- 总结了基于EC的分子和模块DB识别策略.
- 包括进化算法和生物标志物分析的群体智能.
- 被认为是DB识别问题和EC优势的基本特征.
主要成果:
- 提供了适用于多态数据的EC方法的全面审查.
- 突出了EC在解决生物标志物发现复杂性的潜力.
- 针对不同类型的生物标志物识别的分类EC方法.
结论:
- 在生物标志物发现中,EC提供了一个有前途的框架来分析复杂的多组数据.
- 需要进一步的研究来克服基于EC的生物标志物识别的当前挑战.
- 这项工作促进了EC和生物信息学之间的跨学科融合.
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