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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

98
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
98
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

5.4K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
5.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.2K
The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
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Updated: Jul 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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一个内在可解释的神经网络架构,用于序列到功能学习.

Ali Tuğrul Balcı1,2, Mark Maher Ebeid1,2, Panayiotis V Benos3

  • 1Joint Carnegie Mellon University-University of Pittsburgh Program in Computational Biology, Pittsburgh, PA 15213, United States.

Bioinformatics (Oxford, England)
|June 30, 2023
PubMed
概括
此摘要是机器生成的。

我们为基因组学开发了一种完全可解释的序列到功能模型 (tiSFM). 这种可解释的深度学习模型可以预测功能性基因组读数,性能提高,参数比标准方法少.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 基于序列的深度学习模型预测了基因组功能,但缺乏可解释性.
  • 目前的方法需要计算密集的后期分析来解释模型.
  • 高度参数化的模型往往会掩盖内部机制.

研究的目的:

  • 引入一种新的深度学习架构,即完全可解释的序列到函数模型 (tiSFM).
  • 与标准卷积模型相比,提高模型性能和参数效率.
  • 允许模型参数与序列图案相关的内在解释.

主要方法:

  • 开发了tiSFM深度学习架构.
  • 应用tiSFM来分析跨血造细胞类型的开放色素测量.
  • 将tiSFM性能与最先进的卷积神经网络进行比较.

主要成果:

  • 在开放的染色质数据上,tiSFM在量身定制的卷积神经网络上表现出卓越的性能.
  • 确定了对血液形成分化至关重要的特定背景转录因子活动 (例如,Pax5,Ebf1,Rorc).
  • tiSFM参数为预测发育过程中的表观遗传状态变化提供了生物学上有意义的解释.

结论:

  • tiSFM为基因组学中的标准深度学习模型提供了一个可解释的替代方案.
  • 该模型的可解释参数为对序列功能关系的生物学洞察提供了便利.
  • tiSFM对于复杂的任务是有效的,比如预测发育表观遗传过渡.