This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The ‘MVME’ model is assessed using the O’Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC). This paper presents an enhanced synergistic drug combination model by utilizing four different feature views for drugs and four views for cancer cell lines. Each view is then input into a multi-task deep learning model to predict both the synergy score and class label simultaneously. To address the challenge of managing diverse views and their corresponding prediction values while avoiding overfitting, an ensemble model is applied.

中文翻译：

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基于集成和多任务注意模型的预测药物组合协同作用的多视图特征表示


本文提出了一种新颖的多视图集成预测模型，旨在通过预测药物组合与癌细胞系的协同分数值和协同类别标签来解决确定协同药物组合的挑战。所提出的方法涉及通过四个不同的视图来表示药物特征：简化分子输入线路输入系统（SMILES）特征、分子图特征、指纹特征和药物靶标特征。另一方面，细胞系特征通过四个视图来捕获：基因表达特征、拷贝数特征、突变特征和蛋白质组学特征。为了防止模型过度拟合，采用了两种技术。首先，药物的每个视图特征与每个相应的细胞系视图配对，并输入到多任务注意力深度学习模型中。该多任务模型经过训练可以同时预测协同得分值和协同类别标签。该过程导致十六个输入视图特征被输入到多任务模型中，产生十六个预测值。随后，这些预测值被用作集成模型的输入，该模型输出最终的预测值。 “MVME”模型使用 O'Neil 数据集进行评估，其中包括 39 种不同癌细胞系中组合的 38 种不同药物，输出 22,737 个药物组合对。对于协同评分值，所提出的模型的均方误差（MSE）为206.57，均方根误差（RMSE）为14.30，皮尔逊评分为0.76。对于协同类标签，模型的准确度得分为 0.90，精确度得分为 0.96，kappa 得分为 0.57，ROC 曲线下面积 (ROC-AUC) 得分为 0.96，精确率-召回率曲线下面积 (PR-AUC) 得分为 0.88 ）。 本文通过利用四种不同的药物特征视图和四种癌细胞系视图，提出了一种增强的协同药物组合模型。然后将每个视图输入到多任务深度学习模型中，以同时预测协同分数和类别标签。为了解决管理不同视图及其相应预测值同时避免过度拟合的挑战，应用了集成模型。