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相关概念视频

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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...
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相关实验视频

Updated: May 29, 2025

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深度Aptamer:通过混合深度学习模型推进高亲和度的aptamer发现.

Xin Yang1,2,3,4, Chi Ho Chan1,2,3, Shanshan Yao3,5

  • 1Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.

Molecular therapy. Nucleic acids
|February 3, 2025
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概括

一个新的混合神经网络DeepAptamer比传统方法更快地识别高亲和度寡核酸aptamer. 这种人工智能模型显著减少了长时间选择轮的需要,加速了体发现.

关键词:
在这里,我们可以看到AIAIAI.的 DNA 序列.DNA 形状特征 DNA 形状特征MT:寡核酸:治疗方法和应用.这就是SELEXEX.它们是Aptamers.发现药物的发现.混合神经网络是一种神经网络.

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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.
  • 分子生物学分子生物学

背景情况:

  • 通过指数式丰富 (SELEX) 的联体的系统演化是长时间的过程,通常需要20-30轮的aptamer识别.
  • SELEX可能会受到实验偏差和非特异性相互作用的影响,可能会排除高亲和度的aptamer候选物,并导致高失败率.

研究的目的:

  • 开发一种计算方法,DeepAptamer,用于从早期的,未经丰富的SELEX轮中识别高 afinity的aptamer序列.
  • 为了加快aptamer发现过程,克服传统SELEX的局限性.

主要方法:

  • 混合神经网络模型DeepAptamer集成了卷积神经网络和双向长短期记忆.
  • 该模型利用序列组成和结构特征来预测阿普坦酶结合亲缘关系并识别结合动机.
  • 在全面的SELEX数据上接受培训,以提高预测准确度.

主要成果:

  • 与现有的模型相比,DeepAptamer在预测阿巴胺结合亲和度方面表现出更高的准确性.
  • 该模型成功地确定了关键的核酸,这些核酸对于目标结合至关重要.
  • 实验验证证证实了DeepAptamer能够有效地识别高亲和度的体.

结论:

  • DeepAptamer显著降低了用于aptamer识别所需的代选择轮的数量,从20-30个减少到早期阶段.
  • 这一进步简化了对各种点的体发现,为生物技术和医学提供了广泛的应用.
  • DeepAptamer 代表了阿普坦技术的飞跃,提高了序列识别的效率和成功率.