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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Peptide Bonds02:43

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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Associative Learning01:27

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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.
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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只有用积极的例子来学习的特性.

Mehrad Ansari1, Andrew D White1

  • 1Department of Chemical Engineering, University of Rochester, Rochester, NY, 14627, USA.

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|June 19, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的半监督深度学习方法,使用积极未标记的学习来发现. 它只使用积极的例子有效地预测性质,克服了传统方法中的数据限制.

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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 深度学习模型需要正面和负面的例子来准确预测.
  • 基数据库往往缺乏足够的负面例子,阻碍了模型开发.
  • 高通量选方法难以有效地识别负的例子.

研究的目的:

  • 开发一个半监督的深度学习框架,用于使用有限的积极例子来预测性质.
  • 为应对基数据库中负面数据不足的挑战.
  • 通过积极未标记的学习来发现具有所需抗微生物特性的序列.

主要方法:

  • 采用正面未标记 (PU) 学习,一种半监督的方法.
  • 采用了两种策略:调整基础分类器和可靠的负识别.
  • 建立了基于序列的深度学习模型来推断的溶解性,血液溶解,SHP-2结合和非污染活性.

主要成果:

  • 该PU学习方法实现了竞争力的预测性能.
  • 该模型仅使用积极数据成功预测了的特性.
  • 性能与经典的正负 (PN) 分类方法相提并论.

结论:

  • 当负面数据稀缺时,半监督学习,特别是PU学习是可行的替代方案.
  • 这种方法使有效的性质预测和发现具有有限的积极例子.
  • 开发的深度学习模型为设计功能性提供了强大的工具.