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相关概念视频

Combination Therapies and Personalized Medicine02:50

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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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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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BioSift:用于过生物医学摘要的数据集,用于药物重用和临床元分析.

David Kartchner1, Irfan Al-Hussaini1, Haydn Turner1

  • 1Georgia Institute of Technology, Atlanta, Georgia, USA.

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|May 1, 2024
PubMed
概括
此摘要是机器生成的。

一个新的数据集,BioSift,通过对科学摘要进行分类来帮助药物重新使用. 在这项关键任务中,人类注释仍然优于自动化方法.

关键词:
积极学习是积极学习.文件过是指对文件进行过.药物重用是为了改变药物的用途.监管能力较弱 监管能力较弱

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 药物再利用加速了对现有药物的新治疗用途的识别.
  • 有效地选和标记科学文献是药物重新利用的瓶.
  • 患者干预比较结果 (PICO) 框架对于元分析至关重要.

研究的目的:

  • 介绍BioSift,一个新的,人类注释的数据集用于文档分类.
  • 促进药物重用研究的研究的初步选择和标签.
  • 为评估自动化文档分类方法提供一个基准.

主要方法:

  • 策划了10,000个PubMed摘要与人类注释.
  • 有注释的摘要最多有八个与PICO相关的属性.
  • 通过多个注释者和高级审查来确保数据质量.

主要成果:

  • 证明PubMed的先进过器和当前最先进的分类模型都无法取代人类注释.
  • 在BioSift数据集上建立了强大的基准结果.
  • 提供数据统计,包括审查员协议和标签共发生情况.

结论:

  • 生物Sift提出了一个关键但具有挑战性的文件分类任务,以加快药物重新用途.
  • 人类注释目前对于药物重新定位的准确研究选择是不可或缺的.
  • 公共可用的BioSift数据集将推动药物发现的文档分类算法研究.