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用基于变压器的神经网络从文本数据中提取的miRNA-疾病关系数据集
Sumit Madan1, Lisa Kühnel2,3, Holger Fröhlich1,4
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.
Database : the journal of biological databases and curation
|August 6, 2024
概括
这项研究引入了一种深度学习方法,自动识别微RNA (miRNA) 和科学文本中的疾病联系. 该方法准确地提取了新的miRNA疾病关联,有助于研究神经退行性疾病等疾病.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 微RNAs (miRNAs) 是细胞功能的关键调节者,它们的失调与癌症和神经退行性疾病等疾病有关.
- 从广泛的生物医学文献中识别miRNA疾病关联是具有挑战性的,因为手工提取的时间和劳动强度.
研究的目的:
- 开发和评估基于深度学习的文本挖掘方法,用于提取正常化的miRNA-疾病关联.
- 从多个数据库中使用远程监督创建一个扩展的培训群体.
主要方法:
- 一个深度学习模型被训练在一个与远程监督增强的新型体上.
- 该方法被定量评估,以检测miRNA-疾病关联的能力.
- 该模型的适用性通过从PubMed和PubMed Central中提取关联来证明.
主要成果:
- 开发的工作流在测试组上实现了接受器操作员特征曲线下的98%的面积.
- 该方法成功地提取了新的miRNA疾病关联,特别是神经退行性疾病.
- 这些提取的关联以前没有出现在公共数据库中.
结论:
- 深度学习的文本挖掘方法有效而准确地从生物医学文献中提取miRNA疾病关联.
- 这种方法为研究人员发现新的与疾病相关的miRNA提供了宝贵的工具,加速了生物医学研究.
- 这些发现突出了AI在从非结构化数据中揭示复杂的生物关系方面的潜力.
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