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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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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Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
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α-Helix containing multi-pass transmembrane proteins
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多AOP:一种轻量级的多视图深度学习框架,用于抗氧化发现.

Jianxiu Cai1,2, Xinpo Lou1,3, Chak Fong Chong1,2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau SAR, China.

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概括

发现抗氧化 (AOP) 对健康和食品保存至关重要. 一个新的深度学习框架,Multi-AOP,有效地使用序列和图形数据识别AOP,优于现有的方法.

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科学领域:

  • 生物化学和生物信息学
  • 计算化学计算化学
  • 人工智能在药物发现中的作用

背景情况:

  • 抗氧化 (AOPs) 显示出疾病预防和食品保存的潜力,因为它们的自由基清除特性.
  • 发现AOP的传统实验方法效率低下,资源密集.

研究的目的:

  • 为增强抗氧化剂的发现开发一个高效的计算框架.
  • 整合序列和结构信息,以提高AOP预测的准确性.

主要方法:

  • 开发了Multi-AOP,一个轻量级的多视图深度学习框架,使用扩展长期短期内存 (xLSTM) 进行序列嵌入.
  • 在SMILES表示上使用传递信息的神经网络 (MPNN) 来提取分子图的特征,捕获物理化学性质.
  • 实现了序列和图形特征的层次融合,用于全面的类分析.

主要成果:

  • 多AOP实现了高的预测准确度:0.8043 (AnOxPePred),0.9684 (AnOxPP) 和0.9043 (AOPP). 通过多AOP实现了高的预测准确度:0.8043 (AnOxPePred),0.9684 (AnOxPP),以及0.9043 (AOPP).
  • 该框架始终优于传统的机器学习和最先进的深度学习方法.
  • 创建了一个统一的AOP数据集,以促进可概括的AOP预测模型的开发.

结论:

  • 多AOP框架在有效和准确的抗氧化发现方面取得了重大进展.
  • 综合序列和图形学习为预测功能提供了强大的方法.
  • 公共可访问的数据集和模型将加速AOP开发的未来研究.