ISCMF: Integrated similarity-constrained matrix factorization for drug-drug interaction prediction
ISCMF: Integrated similarity-constrained matrix factorization for drug-drug interaction prediction
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DOI：
10.1007/s13721-019-0215-3
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发表时间：
2020-01-10
影响因子：
2.3
通讯作者：
Katanforoush, Ali
中科院分区：
文献类型：
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作者：
Rohani, Narjes;Eslahchi, Changiz;Katanforoush, Ali
药物相互作用(DDI)预测为药物发现提供了大量的信息。由于DDIS的准确预测可以降低人类的健康风险，因此开发一种准确的方法来解决这一问题具有十分重要的意义。尽管该领域进行了大量研究，但仍有相当数量的DDIS尚未鉴定。在本研究中，我们使用集成相似性约束矩阵因式分解(ISCMF)来预测DDIS。根据药物的亚结构、靶点、副作用、标签外副作用、途径、转运体、酶和适应症数据以及药物对的高斯相互作用曲线，计算了8个相似性。随后，采用非线性相似度融合方法对多个相似度进行融合，使其具有更强的信息量。最后，我们使用了ISCMF，它将相互作用空间中的药物投影到低排名空间，以获得对DDIS的新见解。然而，以前的研究已经提出了ISCMF的所有部分，但我们的创新之处在于将它们应用于DDI预测环境中，并将它们结合在一起。我们将ISCMF与几种最先进的方法进行了比较。结果表明，该方法在五次交叉验证中取得了较好的效果。它将AUPR和F-MEASure分别提高到10%和18%。为了进一步验证，我们对ISCMF预测的大量高概率相互作用进行了案例研究，其中大部分得到了可靠的数据库的验证。我们的结果支持这样的观点，即ISCMF可能被明确地用作预测未知DDI的一种有效方法。有关ISCMF的数据和实施情况，请访问。
Drug-drug interaction (DDI) prediction prepares substantial information for drug discovery. As the exact prediction of DDIs can reduce human health risk, the development of an accurate method to solve this problem is quite significant. Despite numerous studies in the field, a considerable number of DDIs are not yet identified. In the current study, we used Integrated Similarity-constrained matrix factorization (ISCMF) to predict DDIs. Eight similarities were calculated based on the drug substructure, targets, side effects, off-label side effects, pathways, transporters, enzymes, and indication data as well as Gaussian interaction profile for the drug pairs. Subsequently, a non-linear similarity fusion method was used to integrate multiple similarities and make them more informative. Finally, we employed ISCMF, which projects drugs in the interaction space into a low-rank space to obtain new insights into DDIs. However, all parts of ISCMF have been proposed in previous studies, but our novelty is applying them in DDI prediction context and combining them. We compared ISCMF with several state-of-the-art methods. The results show that It achieved more appropriate results in five-fold cross-validation. It improves AUPR, and F-measure to 10% and 18%, respectively. For further validation, we performed case studies on numerous interactions predicted by ISCMF with high probability, most of which were validated by reliable databases. Our results provide support for the notion that ISCMF might be used unequivocally as a powerful method for predicting the unknown DDIs. The data and implementation of ISCMF are available at .