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Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network.
- Source :
-
Computational Biology & Chemistry . Jun2023, Vol. 104, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /− 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations. [Display omitted] • Microbes in the human body are closely associated with many complex human diseases and are emerging as new drug targets. • Sparse learning methods are used to decompose the matrix to extract data information. • Multiple sources of data are introduced and a generic global network update algorithm is built to optimize the model. • Model evaluation results show the predictive advantage of MFTLHNMDA and its good generalization performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATRIX decomposition
*DRUG target
*DRUG development
*HUMAN body
*THERAPEUTICS
Subjects
Details
- Language :
- English
- ISSN :
- 14769271
- Volume :
- 104
- Database :
- Academic Search Index
- Journal :
- Computational Biology & Chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 163948222
- Full Text :
- https://doi.org/10.1016/j.compbiolchem.2023.107857