Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions
复制标题
DOI：
10.1145/2487575.2487670
复制
发表时间：
2013-08
期刊：
影响因子：
--
通讯作者：
Xiaodong Zheng;Hao Ding;Hiroshi Mamitsuka;Shanfeng Zhu
中科院分区：
文献类型：
--
作者：
Xiaodong Zheng;Hao Ding;Hiroshi Mamitsuka;Shanfeng Zhu
我们解决了从三个输入预测新的药物-靶标相互作用的问题：已知的相互作用、药物的相似性和靶标的相似性。许多方法都考虑了这种设置，但它们有一个共同的问题，即只允许有一个药物和靶点的相似性矩阵。我们方法的关键思想是使用多个药物和靶点的相似性矩阵，其中根据数据估计多个相似性矩阵的权重以自动选择相似性，这对于提高预测药物-靶点相互作用的性能是有效的。我们提出了一种名为多重相似性协作矩阵分解（MSCMF）的因子模型，它将药物和靶标投影到一个共同的低秩特征空间中，这进一步与药物和靶标的加权相似性矩阵保持一致。这两个低秩矩阵和相似性矩阵的权重通过交替最小二乘算法进行估计。我们的方法允许通过两个低秩矩阵协同预测药物-靶标相互作用，并检测对于预测药物-靶标相互作用很重要的相似性。这种方法是通用的，适用于任何具有元素相似性的二元关系，在许多应用程序中都可以找到，例如推荐系统。事实上，MSCMF 是一类协同过滤的加权低秩近似的扩展。我们使用合成数据集和真实数据集广泛评估了 MSCMF 的性能。实验结果表明，MSCMF 在选择相似性方面具有良好的特性，有助于提高预测性能，并且 MSCMF 相对于预测药物-靶标相互作用的六种最先进方法具有性能优势。
We address the problem of predicting new drug-target interactions from three inputs: known interactions, similarities over drugs and those over targets. This setting has been considered by many methods, which however have a common problem of allowing to have only one similarity matrix over drugs and that over targets. The key idea of our approach is to use more than one similarity matrices over drugs as well as those over targets, where weights over the multiple similarity matrices are estimated from data to automatically select similarities, which are effective for improving the performance of predicting drug-target interactions. We propose a factor model, named Multiple Similarities Collaborative Matrix Factorization(MSCMF), which projects drugs and targets into a common low-rank feature space, which is further consistent with weighted similarity matrices over drugs and those over targets. These two low-rank matrices and weights over similarity matrices are estimated by an alternating least squares algorithm. Our approach allows to predict drug-target interactions by the two low-rank matrices collaboratively and to detect similarities which are important for predicting drug-target interactions. This approach is general and applicable to any binary relations with similarities over elements, being found in many applications, such as recommender systems. In fact, MSCMF is an extension of weighted low-rank approximation for one-class collaborative filtering. We extensively evaluated the performance of MSCMF by using both synthetic and real datasets. Experimental results showed nice properties of MSCMF on selecting similarities useful in improving the predictive performance and the performance advantage of MSCMF over six state-of-the-art methods for predicting drug-target interactions.