pathCLIP:通过图像-文本对比学习从生物路径图表中检测基因和基因关系
Fei He1, Kai Liu2, Zhiyuan Yang2
1School of Information Science and Technology, Northeast Normal University, Changchun 130000, China; Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia Missouri, MO 65211 USA.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
这项研究介绍了pathCLIP,这是一个用于从生物医学途径图中提取基因和基因关系的新系统. 通过整合图像和文本数据,pathCLIP增强了精准医学的生物通路治疗.
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
- 生物医学信息学 生物医学信息学
背景情况:
- 生物学途径对于理解生物机制和精密医学至关重要,在文献中经常使用图像和文本来描绘.
- 现有的路径信息提取方法经常单独分析图像和文本,忽视它们的相互联系.
- 全面的路径知识库是必不可少的,但由于各种来源的数据碎片化,因此构建具有挑战性.
研究的目的:
- 开发和验证pathCLIP,一种用于识别生物医学文献中的基因和基因关系的通路图表策划系统.
- 利用多模式方法,特别是图像-文本对比学习,以有效地将视觉元素与路径图中的文本描述联系起来.
- 展示该系统从多个文献来源中策划路径信息的能力,并增强现有的数据库,如KEGG.
主要方法:
- 实现pathCLIP,一个使用图像-文本对比学习模型的系统.
- 训练模型对图像片段和基因和基因关系的相应文本描述的协调嵌入进行训练.
- 使用PubMed来源的路径数据进行验证,将多式联络方式的性能与单式联络方式的性能进行比较.
主要成果:
- 采用多模式方法的pathCLIP系统在路径信息策划方面表现出优异的性能,与单模式模型相比.
- 该系统成功地从各种生物医学文献中识别和策划了基因及其关系.
- 一个涉及非小细胞肺癌文献的案例研究强调了该系统在丰富KEGG数据库方面的有效性.
结论:
- 路径CLIP的多模式方法显著提高了从文献中对生物路径策划的准确性和全面性.
- pathCLIP提供了一个强大的解决方案,用于将视觉和文本信息集成到路径图中,从而推进知识库的构建.
- 精选的途径数据具有增强下游应用的潜力,包括精准医学和途径数据库的丰富.
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