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相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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The perception of a salty flavor is facilitated by sodium ions within the oral salivary fluid. Upon consumption of a salty substance, salt crystals disassemble, leading to the liberation of its constituents—Na+ and Cl- ions. These ions subsequently dissolve into the salivary fluid present in the oral cavity. The external environment of the gustatory cells experiences an elevation in Na+ concentration, thereby establishing a potent concentration gradient. This gradient propels the...
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Gustation is a chemical sense that, along with olfaction (smell), contributes to our perception of taste. It starts with the activation of receptors by chemical compounds (tastants) dissolved in the saliva. The saliva and filiform papillae on the tongue distribute the tastants and increase their exposure to the taste receptors.
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乌玛米-BERT:一种基于BERT的可解释模型,用于预测乌玛米.

Jingcheng Zhang1, Wenjing Yan2, Qingchuan Zhang2

  • 1Food Laboratory of Zhongyuan, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing 100048, China; Key Laboratory of Flavor Science of China Gengeral Chamber of Commerce, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing 100048, China.

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概括

一个新的深度学习模型,Umami-BERT,快速选乌玛米. 这个模型识别了关键的氨基酸,如氨酸和谷氨酸,为未来的酸发现提供了洞察力.

关键词:
从变压器的双向编码器表示.生物信息学是一种生物信息学.深度学习是一种深度学习.功能表示学习学习的特征表示.序列分析是指进行序列分析.乌玛米酸是乌玛米酸的一种.

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科学领域:

  • 生物技术是生物技术.
  • 生物信息学是一种生物信息学.
  • 食品科学 食品科学 食品科学

背景情况:

  • 乌玛米增强了味道和营养,推动了对新型来源的需求.
  • 目前对乌玛米的选方法效率低下,需要先进的方法.
  • 识别乌玛米对于食品创新和营养增强至关重要.

研究的目的:

  • 开发一种深度学习 (DL) 模型,以快速有效地选乌玛米.
  • 创建一个模型,直接从氨基酸序列预测乌玛米的潜力.
  • 为了深入了解乌玛米的分子特性.

主要方法:

  • 开发了Umami-BERT模型,使用双阶段训练策略与变压器的双向编码器表示 (BERT) 和初始网络.
  • 在对广泛的生物活性序进行预训练时,使用了注意力机制.
  • 利用UMP789数据集进行再培训和乌玛米预测.

主要成果:

  • 乌玛米-伯特模型实现了高精度 (93.23%在平衡数据集上,95.00%在不平衡数据集上) 和MCC (0.78,0.85).
  • 与现有模型相比,在预测乌玛米方面表现出卓越的性能.
  • 确定了氨酸 (A),氨酸 (C),酸盐 (D) 和胺酸 (E) 作为关键的氨基酸贡献者.

结论:

  • 乌玛米-伯特为大规模选候选乌玛米提供了一个强大的工具.
  • 该模型为基础的顺序模式提供了新的见解. 乌玛米味道.
  • 促进在食品科学中对功能性的进一步探索和发现.