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相关概念视频

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Targeted Cancer Therapies02:57

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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相关实验视频

Updated: Jun 15, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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推进中国生物医学文本挖掘与社区挑战.

Hui Zong1, Rongrong Wu1, Jiaxue Cha2

  • 1Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.

Journal of biomedical informatics
|August 28, 2024
PubMed
概括
此摘要是机器生成的。

社区在中国生物医学文本挖掘方面的挑战是先进的技术和协作. 这些39项评估任务 (2017-2023) 涵盖了各种自然语言处理应用,推动生物医学信息学领域的创新.

关键词:
人工智能的人工智能是人工智能.生物医学文本挖掘技术处理健康信息 处理健康信息大型语言模型.自然语言处理自然语言处理.

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科学领域:

  • 生物医学信息学是生物医学信息学.
  • 自然语言处理自然语言处理.
  • 计算语言学 计算语言学

背景情况:

  • 生物医学文本挖掘对于从大量文献中提取知识至关重要.
  • 社区挑战是评估和推进文本挖掘技术的重要平台.
  • 近年来,中国的生物医学文本挖掘举措显著增长.

研究的目的:

  • 审查最近在中国生物医学文本挖掘社区挑战方面的进展.
  • 系统地分析评估任务和数据集的情景.
  • 探索这些挑战的临床应用和未来方向.

主要方法:

  • 收集了来自社区挑战的评估任务信息,包括描述,数据集和任务类型.
  • 进行了各种生物医学自然语言处理任务的系统摘要和比较分析.
  • 任务包括命名实体识别,关系提取,文本分类和大型语言模型评估.

主要成果:

  • 从6个社区挑战 (2017-2023) 中确定了39个评估任务.
  • 分析揭示了不同的任务类型和数据源,具有潜在的临床应用.
  • 与英语同行进行了比较,并讨论了贡献,局限性和未来方向.

结论:

  • 社区挑战评价竞赛显著促进生物医学文本挖掘技术创新.
  • 这些挑战促进跨学科的合作,并为开发最先进的解决方案提供平台.
  • 它们有助于推进该领域,特别是随着大型语言模型的兴起.