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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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自动回归酶功能预测与多尺度多模式融合.

Dingyi Rong1, Bozitao Zhong2, Wenzhuo Zheng1

  • 1School of Information and Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.

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概括
此摘要是机器生成的。

我们开发了MAPred,这是一种新的深度学习模型,通过整合蛋白质序列和结构数据来预测酶功能. 这种方法准确地预测了酶委员会 (EC) 的数字,改进了生物信息学分析.

关键词:
自动回归预测的预测.酶功能的预测和预测多种方式的多种方式.多个尺度的多个尺度.

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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
  • 酶学 是一种酶学.

背景情况:

  • 准确的酶功能预测对于生物研究和工业应用至关重要.
  • 当前的深度学习模型通常只使用序列或结构数据,并且未能利用酶委员会 (EC) 数字的层次性.

研究的目的:

  • 引入一种新的多模式,多规模和自动回归的深度学习模型来预测酶功能.
  • 通过整合蛋白质序列和3D结构信息来解决现有方法的局限性.
  • 为了利用EC数的等级结构来更精确地预测函数.

主要方法:

  • 开发了多尺度多模式自回归预测器 (MAPred) 模型.
  • MAPred使用双路径方法整合了初级氨基酸序列和3D结构令牌.
  • 采用自回归网络来顺序预测EC号码的数字,尊重它们的等级分类.

主要成果:

  • 与基准数据集 (New-392,Price,New-815) 上的现有模型相比,MAPred 显示出更高的性能.
  • 该模型有效地捕获了全面的蛋白质特征,并确定了关键的局部功能部位.
  • 在蛋白质功能预测的可靠性和颗粒度方面取得了重大进展.

结论:

  • 通过整合多模式数据和尊重EC数量层次,MAPred代表了预测酶功能的重大进步.
  • 该模型利用序列和结构信息的能力提高了酶功能预测的准确性和细节性.
  • 这种方法为生物信息学研究和与酶相关的应用提供了更强大的工具.