使用文本挖掘方法提取蛋白与蛋白相互作用网络增加了对自闭症谱系障碍的洞察力
Leena Nezamuldeen1,2, Mohsin Saleet Jafri1,3
1School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
Biology
|October 27, 2023
概括
本研究引入了一种自动化系统,用于从生物医学文献中提取蛋白质与蛋白质相互作用 (PPI). 该系统通过准确识别PPI来增强疾病研究,改善对疾病起源的理解.
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
- 文本挖掘 (Text Mining) 是一个很好的方法.
背景情况:
- 生物医学文献的快速扩张需要先进的文本挖掘方法.
- 了解蛋白质与蛋白质相互作用 (PPI) 对于阐明疾病机制至关重要.
- 现有的方法可能无法准确地区分与疾病相关的蛋白质提及与实际的PPI.
研究的目的:
- 开发一个自动化系统,准确地从生物医学文本中提取PPI.
- 提高识别特定描述PPI的句子的精度.
- 为疾病相关蛋白质建立全面的PPI网络.
主要方法:
- 利用深度学习,包括BiLSTM循环神经网络和一个名为实体识别 (NER) 的有条件随机场 (CRF) 模型.
- 通过SpaCy库使用预训练的词嵌入和最短依赖路径 (SDP) 模型.
- 开发了一个句子分类模型,专门针对PPI相关的句子.
主要成果:
- 与之前的BiLSTM模型相比,PPI提取的精度在Aimed/BioInfr体上提高了13%.
- 开发的NER模型在Aimed/BioInfr库中显示了98%的精度.
- 成功地绘制了蛋白质相互作用,以完成七种与自闭症谱系障碍相关的蛋白质的策划PPI网络.
结论:
- 自动化系统准确地提取PPI,有助于理解疾病病因.
- 这种系统产生的PPI网络可以阐明相互作用缺陷在复杂疾病中的作用.
- 这种方法增强了对蛋白质相互作用如何影响生物过程和疾病发展的理解.
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