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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

107
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...
107
RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
17.5K
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

78
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...
78
Next-generation Sequencing03:00

Next-generation Sequencing

89.0K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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相关实验视频

Updated: Jul 13, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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对于生物测序数据的内在可解释的位置感知卷积模式内核网络.

Jonas C Ditz1, Bernhard Reuter2, Nico Pfeifer3

  • 1Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Sand 14, Tübingen, 72076, Germany. jonas.ditz@uni-tuebingen.de.

Scientific reports
|October 11, 2023
PubMed
概括

卷积动机内核网络为医疗保健提供可解释的人工智能. 这种方法为预测提供了生物学上有意义的解释,增强了临床环境中的信任和整合.

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Last Updated: Jul 13, 2025

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科学领域:

  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习
  • 计算生物学 计算生物学

背景情况:

  • 人工神经网络 (ANN) 擅长识别数据相关性,但通常充当"黑子",限制科学理解和信任.
  • 解释性对于医疗保健等高风险领域至关重要,使领域专家能够验证和将AI预测集成到临床实践中.

研究的目的:

  • 引入卷积动机内核网络 (CMKNs),这是一个新的神经网络架构,旨在进行可解释的预测.
  • 能够直接解释预测结果,并提供生物学和医学上有意义的解释,从而消除了对后期分析的需求.

主要方法:

  • 开发了一个神经网络架构学习特征表示在复制内核的希尔伯特空间使用位置感知图案内核函数.
  • 采用端到端的学习方案,从数据中直接提取生物学意义上的概念.

主要成果:

  • 在小数据集上表现出强大的学习能力.
  • 在使用DNA和蛋白质序列的医疗预测任务中实现了最先进的性能.
  • 展示了模型直接从数据中学习生物学上有意义的概念的能力.

结论:

  • 在医疗保健和生物信息学中,CMKN为传统的ANN提供了可解释的替代方案.
  • 该模型的直接可解释性和强的性能促进了对临床和研究环境的信任和采用.
  • 适用于生物序列数据 (DNA,蛋白质),以推进医学和生物见解.