DeepCoVDR:使用图形变压器和交叉注意力进行深度转移学习,用于预测COVID-19药物反应
Zhijian Huang1, Pan Zhang2, Lei Deng1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
DeepCoVDR是一种新的深度转移学习方法,可以准确预测COVID-19药物反应. 这种方法有助于通过分析药物和细胞系相互作用来发现有效的抗病毒COVID-19治疗方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 机器学习用于医学.
背景情况:
- 新冠肺炎2019 (COVID-19) 疫情迫切需要开发有效的抗病毒药物.
- 现有的COVID-19治疗方法可能不足,特别是对于患有并发病的人来说.
- 准确预测药物反应对于确定安全有效的COVID-19治疗非常重要.
研究的目的:
- 提出DeepCoVDR,这是一种用于预测COVID-19药物反应的新型深度转移学习方法.
- 为了利用图形变换器和交叉注意力机制来加强药物向相互作用分析.
- 通过利用癌症数据集的转移学习来解决SARS-CoV-2数据稀缺问题.
主要方法:
- 开发了DeepCoVDR,使用图形变压器和前神经网络来处理药物和细胞系数据.
- 实施了交叉注意模块来建模药物细胞系相互作用.
- 员工转移学习通过癌症数据集的预训练和SARS-CoV-2数据集的微调.
主要成果:
- 在预测COVID-19药物反应的回归和分类任务中,DeepCoVDR在基线方法中表现优越.
- 该模型在癌症数据集上实现了高性能,表明其可概括性.
- 从FDA批准的药物库中成功识别出潜在的新型COVID-19药物.
结论:
- DeepCoVDR是预测COVID-19药物反应的有效方法,解决迫切需要新的治疗方法.
- 转移学习方法成功克服了病毒性疾病研究中的数据稀缺问题.
- 在加快针对COVID-19和其他疾病的新型抗病毒药物的发现方面,DeepCoVDR显示出前景.
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