矩阵分解
计算机科学
双线性插值
成对比较
非负矩阵分解
特征(语言学)
药物发现
相似性(几何)
数据挖掘
药物开发
基质(化学分析)
人工智能
药品
算法
模式识别(心理学)
机器学习
药物重新定位
生物信息学
图像(数学)
医学
材料科学
复合材料
精神科
特征向量
哲学
物理
生物
量子力学
语言学
计算机视觉
作者
Mengyun Yang,Gaoyan Wu,Qichang Zhao,Yaohang Li,Jianxin Wang
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI