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相关概念视频

Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

7.9K
Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
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Associative Learning01:27

Associative Learning

388
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
388
Allosteric Regulation01:08

Allosteric Regulation

58.0K
Allosteric regulation of enzymes occurs when the binding of an effector molecule to a site that is different from the active site causes a change in the enzymatic activity. This alternate site is called an allosteric site, and an enzyme can contain more than one of these sites. Allosteric regulation can either be positive or negative, resulting in an increase or decrease in enzyme activity. Most enzymes that display allosteric regulation are metabolic enzymes involved in the degradation or...
58.0K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

71
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

147
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
147

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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对于模型-异质联合学习的Allosteric特征协作.

Baoyao Yang, Pong C Yuen, Yiqun Zhang

    IEEE transactions on neural networks and learning systems
    |December 25, 2023
    PubMed
    概括

    本研究介绍了用于模型异质联合学习 (M-hete FL) 的Allosteric特征协作 (AlFeCo). AlFeCo能够在结构不同的客户端模型之间进行知识交换,从而提高协作学习的性能.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 分布式系统 分布式系统

    背景情况:

    • 联合学习 (FL) 通常假设同质的客户端模型,限制其应用.
    • 在FL (M-hete FL) 中的模型异质性存在挑战,原因是无法直接汇总各种客户端模型参数.

    研究的目的:

    • 提出一种新的方法,即Allosteric Feature Collaboration (AlFeCo),用于在模型异质的联合学习环境中有效的协作学习.
    • 通过在结构上不同的客户端模型之间实现知识交换,解决M-hete FL中的参数聚合的挑战.

    主要方法:

    • 开发了一个全特征生成器,从多个客户端模型中提取与任务相关的信息.
    • 利用客户端共享和客户端特定的代码进行知识交换和生成尺寸可变的全特征.
    • 实施了双路通信机制 (模型-模型和模型-预测),以监督使用全特征的协作模型更新.

    主要成果:

    • AlFeCo有效地促进了异质客户模式之间的知识交流.
    • 拟议的方法在经典的FL基准指标上表现强.
    • 在模型异质的联合反伪造任务中,AlFeCo被证明是有效的.

    结论:

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    • AlFeCo提供了一个可行的解决方案,用于在模型异构的联合学习环境中进行协作学习.
    • 该方法增强了不同客户模式之间的沟通和知识共享.
    • 理论证据和趋同分析支持AlFeCo在M-hete FL中的有效性.