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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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DNA as a Genetic Template02:05

DNA as a Genetic Template

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DNA as a Genetic Template02:05

DNA as a Genetic Template

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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Law of Independent Assortment02:03

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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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概率上对齐的视图-未对齐的集群与自适应模板选择

Wenhua Dong, Xiao-Jun Wu, Zhenhua Feng

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    这项研究引入了一种新的视图不对齐聚类方法,解决了从独立来源匹配数据的挑战. 拟议的概率一致的视图不一致的集群与自适应模板选择 (PAVuC-ATS) 有效地恢复交叉视图对应,以改善表示学习.

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    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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    相关实验视频

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    Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 交叉视图对应 (CVC) 对于多视图建模至关重要,但由于独立的数据组织 (视图未对齐问题,VUP) 而经常失败.
    • 恢复非对齐的多视图数据的CVC是机器学习中的一个重大,未得到解决的挑战.

    研究的目的:

    • 通过恢复交叉视图对应,开发一种可靠的方法来聚类视图不对应的数据.
    • 在面对独立数据流时,解决现有的多视图建模技术的局限性.

    主要方法:

    • 拟议的概率上对齐的视图未对齐的集群与自适应模板选择 (PAVuC-ATS).
    • 在二分位图范式内进行集成的排列推导,用于视图不对齐的集群.
    • 通过重新制定隐性表示对齐来实现概率对齐,作为一个具有自适应模板选择的2步马尔科夫链过渡.

    主要成果:

    • 证明了PAVuC-ATS在恢复未对齐数据的交叉视图对应的有效性.
    • 在理论和实验上验证了优化问题的趋同.
    • 与基线方法相比,在六个基准数据集中实现了优异的性能.

    结论:

    • PAVuC-ATS提供了一种强大的解决方案,用于聚类视图未对齐的数据,克服传统CVC先决条件的局限性.
    • 该方法在处理和分析独立组织的多视图数据方面取得了重大进展.