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相关概念视频

Amyloid Fibrils03:03

Amyloid Fibrils

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Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
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Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections
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开发基于机器学习的amyloidogenicity预测器,使用Cross-Beta DB.

Valentin Gonay1,2, Michael P Dunne2, Javier Caceres-Delpiano3

  • 1CRBM UMR 5237 CNRS, Université Montpellier, Montpellier, France.

Alzheimer's & dementia : the journal of the Alzheimer's Association
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PubMed
概括
此摘要是机器生成的。

开发了一个自然存在的粉样蛋白和机器学习 (ML) 预测器的新数据库. 这种预测器,Cross-Beta,在识别氨基酸形成蛋白质方面表现出高精度,有助于评估神经退行性疾病的风险.

关键词:
在GWAS中,GWAS就是GWAS.氨基粉症是什么?人工智能的人工智能是人工智能.计算方法 计算方法交叉β结构的结构.我们的数据库数据库数据库数据库.机器学习就是机器学习.

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A Tailored HPLC Purification Protocol That Yields High-purity Amyloid Beta 42 and Amyloid Beta 40 Peptides, Capable of Oligomer Formation
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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 蛋白质氨基代与各种疾病和生物功能有关.
  • 准确的amyloidogenicity计算预测对于理解这些过程至关重要.
  • 人工智能驱动的预测器的性能严重依赖于训练数据的质量.

研究的目的:

  • 创建自然发生的交叉β粉样蛋白质的高质量数据库.
  • 开发和对机器学习 (ML) 算法进行基准测试,以预测蛋白质粉样蛋白形成潜力.
  • 引入一种新的计算工具来评估氨基原性.

主要方法:

  • 构建Cross-Beta DB,这是已知的交叉β粉样蛋白的精心策划的数据库.
  • 使用交叉贝塔数据库数据集对多个ML算法的训练和评估.
  • 开发使用额外树木ML算法的交叉贝塔预测器.

主要成果:

  • 与现有方法相比,Cross-Beta预测器取得了更高的性能.
  • 该预测器显示高F1得分为0.852,准确度为0.844.
  • 开发的ML模型有效地预测了蛋白质的粉样蛋白形成潜力.

结论:

  • 交叉贝塔DB数据库为粉样蛋白研究提供了宝贵的资源.
  • 交叉β预测器提供了一个强大的工具来识别氨基原蛋白.
  • 这一进步可能有助于针对神经退行性疾病和其他粉样蛋白病的个性化风险分析.