School of Computer Science and Engineering, Central South University, China.
Old Dominion University, USA.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa267.
With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug-drug similarities can be measured from target profiles, drug-drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug-disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug-disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug-disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: jxwang@mail.csu.edu.cn Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.
随着高通量技术的发展和生物医学数据的积累，可以从不同方面计算生物实体的先验信息。具体来说，可以从靶标谱、药物相互作用和副作用方面测量药物-药物相似性；同样，可以使用不同的方法和数据源来计算疾病本体，从而得到多种两两疾病相似性的度量。因此，在计算药物再定位中，开发一种动态方法来优化多种相似性的融合过程是一项关键且具有挑战性的任务。在这项研究中，我们提出了一种多相似性双线性矩阵分解（MSBMF）方法，用于预测现有和新型药物有前途的药物相关适应症。我们没有将多种相似性融合到单个相似性矩阵中，而是分别串联药物和疾病的相似性矩阵。应用矩阵分解方法，我们将药物-疾病关联矩阵分解为药物特征矩阵和疾病特征矩阵。同时，使用这些特征矩阵作为基础，提取有效潜在特征来表示药物和疾病相似性矩阵，以推断缺失的药物-疾病关联。此外，这两个分解矩阵受非负分解的约束，以确保完成的药物-疾病关联矩阵具有生物学可解释性。此外，我们通过有效的交替方向乘子算法数值求解 MSBMF 模型。计算实验结果表明，MSBMF 在交叉验证实验中比最先进的药物再定位方法获得了更高的预测准确性。案例研究也证明了我们提出的方法在实际应用中的有效性。
MSBMF 的数据和代码可在 https://github.com/BioinformaticsCSU/MSBMF 上免费获取。
王建新，中南大学计算机科学与工程学院，湖南长沙 410083，中国。电子邮件：jxwang@mail.csu.edu.cn
补充数据可在线获取于 https://academic.oup.com/bib/
