多重采样方案和深度学习提高了药物相互作用信息从文献中检索分析的积极学习表现
Weixin Xie1, Kunjie Fan1, Shijun Zhang1
1Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA.
Journal of biomedical semantics
|May 29, 2023
概括
本研究介绍了从PubMed.com获取药物相互作用信息的积极学习 (AL). 新的采样技术显著提高了DDI识别的准确性,特别是在数据不平衡的情况下.
科学领域:
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 药物相互作用 (DDI) 信息检索 (IR) 是处理生物医学文献的关键任务.
- 从像PubMed这样的大型数据集中识别DDI存在挑战,原因是样本大小不平衡 (在许多负面数据中,很少有积极的DDI实例).
- 首次探讨积极学习 (AL),以提高DDI IR的效率和准确性.
研究的目的:
- 调查积极学习 (AL) 策略对改善药物相互作用 (DDI) 的有效性,从PubMed摘要中检索信息.
- 通过开发和评估新型采样技术,解决DDI IR中数据集不平衡的挑战.
- 将传统机器学习 (支持矢量机器) 的性能与使用AL的DDI IR中的深度学习方法进行比较.
主要方法:
- PubMed摘要被分为"选" (包含DDI关键字) 和"未选"的池.
- 在积极学习框架内,采用了各种采样方案,包括相似性采样,不确定性采样,随机负面采样和正面采样.
- 支持矢量机 (SVM) 和深度学习模型的性能被评估为0.95.5的回忆率.
主要成果:
- 在选池中,相似性抽样与不确定性抽样相结合,精度从0.89提高到0.92 (SVM).
- 在未选池中,综合采样 (随机负数,正数,相似性) 从0.72提高精度到0.81 (SVM).
- 深度学习模型在两个池中始终优于SVM,达到更高的精度 (例如,在选池中0.96与0.92).
结论:
- 集成先进的采样方案和深度学习算法显著增强了生物医学文献中的DDI IR.
- 随机阴性和阳性抽样方法在极端阶级失衡的情况下改善AL特别有效.
- 该研究表明,通过应用主动学习和深度学习,DDI IR精度得到了显著改善.
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