神经元和神经网络用于模拟蛋白质和蛋白质网络
Sandhya Samarasinghe1, Tran Nguyen Minh-Thai2, Komal Sorthiya3
1Complex Systems, Big Data and Informatics Initiative (CSBII), Lincoln University, New Zealand.
Bio Systems
|October 10, 2025
概括
自动关联神经网络 (ANNN) 有效地模拟蛋白质网络. 带有西格神经元的ANNN精确捕捉了系统动态和蛋白质行为,证明了它们在系统生物学中的潜力.
科学领域:
- 计算系统生物学 计算机系统生物学
- 人工智能在生物学中的应用
背景情况:
- 蛋白质相互作用网络对于细胞功能至关重要.
- 建模这些复杂的网络对于理解生物系统至关重要.
研究的目的:
- 评估用于表示和预测蛋白质网络动态的自关联神经网络 (ANNN).
- 评估ANNN与不同类型神经元 (线性,非线性,二进制) 在建模生物系统中的性能.
主要方法:
- 通过使用普通微分方程 (ODE) 和哺乳动物细胞周期布尔模型的数据来训练ANNN.
- 测试了ANNN识别未知的相互作用和预测蛋白质行为的能力.
- 评估了用于记忆保留的重复性ANNN和单独训练的神经元,用于组装到混合模型中.
主要成果:
- 西格莫伊德ANNN准确地捕获了系统动态和蛋白质行为.
- 线性ANNN模拟了70%的蛋白质,而西格形ANNN模拟了所有蛋白质.
- 二进制ANNN成功复制了布尔规则,混合模型简化了预测.
结论:
- 特别是与西格神经元的ANNN,在建模蛋白质网络动态方面表现出高准确性.
- 混合ANNN模型为预测生物系统行为提供了一种简化方法.
- 循环ANNN显示了强大的记忆能力,用于动态系统建模.
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