选:简化了NER和RE模型的协作学习,以发现基因疾病关系
Minjun Park1, Chan Ung Jeong1, Young Sang Baik2
1Standigm Inc., Seoul, South Korea.
PloS one
|November 27, 2023
概括
我们开发了SCREENER,这是一种从文本中识别基因疾病关系的AI模型,其性能优于现有的方法. 这种工具有助于理解复杂的生物联系,以便更好地诊断和治疗疾病.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 识别基因疾病关系对于临床诊断,治疗和药物设计至关重要.
- 基于文档的关系提取方法在捕捉生物医学文献中远距离单词之间的关系方面面临挑战.
研究的目的:
- 提出基于人工智能的文本挖掘模型,用于学习文档级基因疾病关系.
- 通过将直接和间接关系边缘作为图表可视化来增强模型性能和可解释性.
主要方法:
- 开发了一个AI文本挖掘模型,利用注意力机制来学习文档级别的基因疾病关系.
- 在模型培训期间,纳入了基因目标和疾病之间的直接 (DE) 和间接关系边缘.
- 将关系边缘视觉化为图形,以提高模型的可解释性.
主要成果:
- 获得了0.875的优异F1得分,超过了最先进的文档级提取模型.
- 证明包括直接和间接关系边缘可以提高模型性能.
- 开发了SCREENER网络服务平台,用于实时的基因疾病关系提取.
结论:
- 该SCREENER模型有效地识别了目标基因与高性能疾病之间的生物联系.
- SCREENER平台提供了一个交互式工具,具有图形可视化,用于从生物医学文献中发现和解释基因疾病关系.
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