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相关概念视频

Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
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大规模的神经生物医学实体与层叠写连接.

Tomoki Tsujimura1, Makoto Miwa1, Yutaka Sasaki1

  • 1Computational Intelligence Lab, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku-ku, Nagoya, 468-8511, Aichi, Japan.

Journal of biomedical informatics
|June 29, 2023
PubMed
概括

一个新的神经模型通过层叠写和数据增强来克服数据稀缺性来增强生物医学实体的联系. 这种方法在临床和生物医学概念识别方面取得了最先进的结果.

科学领域:

  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 实体链接对于标准化生物医学信息至关重要,但由于大量的概念和实体提及的变化,出现了挑战.
  • 实体链接的现有神经方法需要广泛的训练数据,这对于数以百万计的生物医学概念来说很难获得.

研究的目的:

  • 为生物医学实体链接开发一种新的神经模型,以解决稀疏训练数据的局限性.
  • 为了提高将生物医学实体提及与数据库条目联系的准确性和效率.

主要方法:

  • 开发了一个纯神经分类模型,结合了层叠加来超越性能上限.
  • 使用数据库条目增强训练数据被用来弥补数据不足.
  • 基于共弦相似度的损失函数被用来有效地区分数百万个生物医学概念.

主要成果:

  • 该系统在n2c2 2019 Track 3挑战中获得了第一名,将临床实体提及与434,056个概念独特标识符 (CUI) 条目联系起来.
  • 该模型在MedMentions数据集 (3.2M概念) 上表现出有效性,并在NLM-CHEM集体 (350K概念) 上实现了新的最先进的性能.

结论:

  • 拟议的神经模型有效地处理生物医学实体链接中的稀疏训练数据.
关键词:
代数相似性 代数相似性数据增强数据增强实体链接实体链接层覆盖的覆盖写入.自然语言处理自然语言处理.

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Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
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  • 该方法为准确分类和链接大量生物医学概念提供了强大的解决方案.