系统生物学中的机械模型和机器学习方法的结合 - - 一个系统的文献综述
Anna Procopio1, Giuseppe Cesarelli2, Leandro Donisi3
1Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italia.
机械建模和机器学习集成为生物系统提供了强大的洞察力. 这种混合方法解决了个体方法的局限性,显示了跨生物尺度的巨大潜力.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 机械模型 (MMs) 和机器学习 (ML) 是系统生物学中宝贵的工具.
- 个别的MM和ML面临着数据要求和计算复杂性等挑战.
- 结合MM和ML提供了一种协同方法来克服这些局限性.
结论:
- 尽管是最近的趋势,但MMs和ML的整合在系统生物学研究中已经很明显.
- 这种混合方法显示出在各种规模上推进生物理解的巨大潜力.
- 对于微观和宏观生物学见解,需要进一步探索这种综合方法.
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