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超图形变压器上的细分级信息融合策略,用于预测抗癌药物的协同效应
Wei Wang1,2, Gaolin Yuan1, Shitong Wan1
1College of Computer and Information Engineering, Henan Normal University, 453007 Xinxiang, China.
这项研究介绍了HypertranSynergy,这是一个新的超图形变压器模型,用于预测协同作用的抗癌药物组合. 它有效地挖掘多颗粒度数据,优于现有的方法来改进癌症治疗选择.
科学领域:
- 计算生物学 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 药物发现
背景情况:
- 组合疗法比单一疗法更有希望,可以提高治疗效率.
- 由于大量的癌症药物,选协同药物组合是昂贵且耗时的.
- 预测协同效应的现有计算方法缺乏探索多颗粒度数据挖掘.
研究的目的:
- 提出一种新的细粒度级信息融合策略,HypertranSynergy,用于预测协同作用的抗癌药物组合.
- 解决现有方法的局限性,有效地挖掘不同细分级别的药物和细胞系数据.
- 提高识别癌症治疗有效药物组合的效率和准确性.
主要方法:
- 开发了HypertranSynergy,这是一个超图形变压器模型,集结了癌症细胞系和药物组合之间的协同连接.
- 实现了一个粗粒度信息提取 (CIE) 模块,用于节点嵌入的变压器,采用Contranorm用于过度平滑缓解和高斯噪声用于本地信息.
- 整合了一个细粒度信息提取 (FIE) 模块,利用药物/细胞系特征的相似性感知矩阵来评估细粒度信息的影响.
主要成果:
- 在分类任务中,HypertranSynergy实现了0.93 ± 0.01的曲线下的面积 (AUC) 和0.69 ± 0.02的精度回忆曲线下的面积 (AUPR).
- 该模型在回归任务中证明了根平均平方误差 (RMSE) 为13.77±0.07和皮尔森相关系数 (PCC) 为0.81±0.02.
- 性能指标在预测协同作用的抗癌药物效应方面超过了大多数最先进的模型.
结论:
- HypertranSynergy有效地整合了多粒度信息,以准确预测协同作用的抗癌药物组合.
- 拟议的模型在计算药物发现方面取得了重大进展,可能减少识别有效组合疗法的成本和时间.
- 这种方法为个性化癌症治疗的更有效,更精确的策略铺平了道路.
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