scholarly journals A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

2015 ◽  
pp. btv544 ◽  
Author(s):  
Zi Yang ◽  
George Michailidis
Keyword(s):  
Matrix Factorization ◽  
Factorization Method ◽  
Modal Data ◽  
Non Negative Matrix Factorization

An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis

2020 ◽  
Vol 53 (7) ◽  
pp. 321-326
Author(s):  
XiaoFeng Gong ◽  
Dongdong Sun ◽  
Zuodong Tang ◽  
Kai Zhou ◽  
RuiSen Luo
Keyword(s):  
Fault Diagnosis ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Non Negative Matrix Factorization

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

scholarly journals Robust semi-supervised non-negative matrix factorization for binary subspace learning

2021 ◽  
Author(s):  
Xiangguang Dai ◽  
Keke Zhang ◽  
Juntang Li ◽  
Jiang Xiong ◽  
Nian Zhang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Original Data ◽  
Factorization Method ◽  
Subspace Learning ◽  
Image Clustering ◽  
The Face ◽  
Factorization Methods ◽  
Salt And Pepper ◽  
Weighted Constraint ◽  
Non Negative Matrix Factorization

AbstractNon-negative matrix factorization and its extensions were applied to various areas (i.e., dimensionality reduction, clustering, etc.). When the original data are corrupted by outliers and noise, most of non-negative matrix factorization methods cannot achieve robust factorization and learn a subspace with binary codes. This paper puts forward a robust semi-supervised non-negative matrix factorization method for binary subspace learning, called RSNMF, for image clustering. For better clustering performance on the dataset contaminated by outliers and noise, we propose a weighted constraint on the noise matrix and impose manifold learning into non-negative matrix factorization. Moreover, we utilize the discrete hashing learning method to constrain the learned subspace, which can achieve a binary subspace from the original data. Experimental results validate the robustness and effectiveness of RSNMF in binary subspace learning and image clustering on the face dataset corrupted by Salt and Pepper noise and Contiguous Occlusion.

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