Predicting drug-target interactions by dual-network integrated logistic matrix factorization.
Predicting drug-target interactions by dual-network integrated logistic matrix factorization.
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DOI：
10.1038/srep40376
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发表时间：
2017-01-12
影响因子：
4.6
通讯作者：
Wang Y
中科院分区：
文献类型：
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作者：
Hao M;Bryant SH;Wang Y
在这项工作中，我们提出了一种双网络集成逻辑矩阵分解（DNILMF）算法来预测潜在的药物-靶标相互作用（DTI）。预测过程包括四个步骤：(1)推断新药/靶点谱，构建谱核矩阵；(2)用药物结构核矩阵扩散药物轮廓核矩阵；(3)用目标序列核矩阵扩散目标剖面核矩阵；(4)建立DNILMF模型并平滑基于邻域的新药/靶点预测。我们将我们的算法与基于基准数据集的最先进的方法进行比较。结果表明，基于10倍交叉验证的5次试验表明，DNILMF算法在精确度召回率曲线下面积（AUPR）和接收者工作特征曲线下面积（AUC）方面优于已有报道的方法。我们得出结论，性能的提高不仅取决于所提出的目标函数，还取决于所使用的非线性扩散技术，这是DTI预测领域中重要的但尚未研究的技术。此外，我们还编译了一个新的DTI数据集，以增加当前可用基准数据集的多样性。新数据集的顶级预测结果得到了实验研究的证实或其他计算研究的支持。
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.