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相关概念视频

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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相关实验视频

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申请说明:基于TD的UFE和基于TD的UFEadv:用于执行基于张量分解的无监督特征提取的生物导体包.

Y-H Taguchi1, Turki Turki2

  • 1Department of Physics, Chuo University, Tokyo, Japan.

Frontiers in artificial intelligence
|September 18, 2023
PubMed
概括

新的R包,基于TD的UFE和基于TD的UFEadv,使得基于张量分解 (TD) 的无监督特征提取 (FE) 可供非专家使用. 这些工具有助于识别差异表达的基因和执行多组学分析,优于现有方法.

关键词:
功能选择 功能选择基因表达的基因表达方式多种多种多种多种多种多种多种多种多种多种.张量分解的分解方式没有监督的学习学习.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 基于张量分解 (TD) 的无监督特征提取 (FE) 是有效的生物信息任务,如生物标志物识别和基因分析.
  • 基于TD的FE的广泛采用一直受到缺乏对没有专业专业知识的研究人员提供用户友好的工具的限制.

研究的目的:

  • 在生物信息学中开发基于TD的无监督FE的可访问工具.
  • 为了使不熟悉TD的研究人员能够进行先进的分析,例如差异基因表达和多组学分析.

主要方法:

  • 开发两个R/生物导体包:基于TD的UFE和基于TD的UFEadv.
  • 在用户友好的接口中实现基于TD的无监督FE算法.
  • 促进差异基因表达分析和多组学数据集成.

主要成果:

  • 开发的包,TDbasedUFE和TDbasedUFEadv,成功地为非专家提供了基于TD的无监督FE.
  • 在相关分析中,基于TD的UFE与最先进的方法,如DESeq2和DIABLO相比,表现优越.
  • 这些软件包促进了关键的生物信息学任务,包括识别差异表达的基因和多组学分析.

结论:

  • 基于TD的UFE和基于TD的UFE降低了在生物信息学研究中利用基于TD的FE方法的入门障碍.
  • 这些软件包为基因组学和多基因组学研究中的特征提取和分析提供了强大且易于使用的替代方案.
  • 这些工具免费使用,促进了该领域的更广泛应用和进步.