Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Feb 7;38(5):1369-1377.
doi: 10.1093/bioinformatics/btab826.

A network-based drug repurposing method via non-negative matrix factorization

Shaghayegh Sadeghi  1 , Jianguo Lu  1 , Alioune Ngom  1
Affiliations

A network-based drug repurposing method via non-negative matrix factorization

Shaghayegh Sadeghi et al. Bioinformatics. .

Abstract

Motivation: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This article presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs.

Results: The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction.

Availability and implementation: The program is available at https://github.com/sshaghayeghs/NMF-DR.

Supplementary information: Supplementary data are available at Bioinformatics online.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
Block diagram of NMF-DR: a computational drug repurposing system
Fig. 2.
Fig. 2.
(a) A demonstration of the impact of SVD-based initialization methods compared with other methods on PREDICT dataset. (b) Comparisons of matrix decomposition algorithms and their accelerated versions on PREDICT dataset
See this image and copyright information in PMC

References

    1. Aggarwal S. et al. (2021) Drug repurposing for breast cancer therapy: old weapon for new battle. Semin. Cancer Biol., 68, 8–20. - PMC - PubMed
    1. Atif S.M. et al. (2019) Improved svd-based initialization for nonnegative matrix factorization using low-rank correction. Pattern Recogn. Lett., 122, 53–59.
    1. Berry M.W. et al. (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal., 52, 155–173.
    1. Bokde D. et al. (2015) Matrix factorization model in collaborative filtering algorithms: a survey. Procedia Comput. Sci., 49, 136–146.
    1. Ceddia G. et al. (2019) Non-negative matrix tri-factorization for data integration and network-based drug repositioning. In: 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE, Siena, Italy. pp. 1–7.

Publication types