一种使用多核积极无标记学习的增强计算方法,用于预测药物向相互作用
Mohammad Reza Keyvanpour1, Soheila Mehrmolaei2, Faraneh Haddadi1,2
1Department of Computer Engineering, Alzahra University, Tehran, Iran.
Current computer-aided drug design
|April 30, 2024
概括
这项研究介绍了MKPUL-BLM,一种用于药物向相互作用预测 (DTIP) 的混合方法. 它通过整合多核和积极的未标记的学习方法来提高预测准确性,解决现有的计算方法中的挑战.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 分析生物网络以预测未来的联系对于药物发现至关重要.
- 药物向相互作用预测 (DTIP) 对于识别潜在的药物向关系至关重要.
- 现有的DTIP计算方法面临诸如缺少负样本和精度低等挑战.
研究的目的:
- 提出一种高效和混合的方法,MKPUL-BLM,以应对DTIP的挑战.
- 提高预测药物向相互作用的准确性和可靠性.
- 为了管理在计算药物发现中缺乏确认的负样本.
主要方法:
- MKPUL-BLM方法结合了多核和积极的未标记的学习 (PUL).
- 它利用网络信息来最大限度地减少小的相似性,并提高准确性.
- 使用PUL生成潜在的负样本,并通过半监督学习扩展标签.
主要成果:
- 该方法在一个旧的相互作用集上实现了0.98的ROCAUC和0.94的AUPR.
- 在一个新的相互作用集上,它将ROCAUC提高到0.89和AUPR提高到0.77.
- 这些结果表明,预测性能显著提高.
结论:
- MKPUL-BLM为药物向相互作用的预测提供了一种高效可靠的替代方案.
- 混合方法有效地解决了以前计算方法的局限性.
- 这种方法有助于通过改进的DTIP推动药物发现.
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