连续KinderMiner (SKiM) 发现和注释生物医学知识使用共发生和变压器模型
bioRxiv : the preprint server for biology
|July 3, 2023
概括
序列KinderMiner (SKiM) 是一种新的基于文献的发现工具,可以有效地发现生物医学概念之间的统计学意义上的联系. SKiM提供了一个灵活的,基于Web的界面,用于探索复杂的关系,改进现有的LBD工具.
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
- 基于文学发现的发现.
背景情况:
- 生物医学研究面临着超过3400万篇PubMed文章的信息过载.
- 有效和可解释的工具对于研究人员来说至关重要,以确定生物医学概念之间的关联.
- 基于文献的发现 (LBD) 旨在揭示孤立的科学文献中隐藏的联系,通常是通过A-B-C关系.
研究的目的:
- 开发串行KinderMiner (SKiM),一个LBD算法和Web接口.
- 解决现有的LBD工具的局限性,包括关系类型识别和可扩展性.
- 为了促进生物医学概念之间的A-B-C关系的发现.
主要方法:
- 开发了SKiM,一个用于找到统计学意义上的A-B-C链接的LBD算法.
- 整合了一个知识图与变压器机器学习模型来解释关系.
- 创建了一个开源的网页界面,包含了全面的生物医学概念列表.
主要成果:
- 证明了SKiM在发现经典LBD,药物重用和与癌症相关的关联方面的实用性.
- SKiM成功地确定了用户定义的概念之间的统计学意义上的关系.
- 综合知识图有助于解释发现的关系的性质.
结论:
- SKiM是LBD的多功能算法,可以在任何领域的用户定义概念之间发现关系.
- 它支持涉及数千个术语的大规模搜索,超越了许多现有的LBD工具的局限性.
- 通过提供来自其知识图的类型标签,SKiM增强了关系识别,超越了简单的存在检测.
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