NDLI: A weighted Bayesian integration method for predicting drug combination using heterogeneous data

Content Provider	Springer Nature : BioMed Central
Author	Li, Tingting Xiao, Long Geng, Haigang Chen, Anqi Hu, Yue-Qing
Abstract	Background In the management of complex diseases, the strategic adoption of combination therapy has gained considerable prominence. Combination therapy not only holds the potential to enhance treatment efficacy but also to alleviate the side effects caused by excessive use of a single drug. Presently, the exploration of combination therapy encounters significant challenges due to the vast spectrum of potential drug combinations, necessitating the development of efficient screening strategies. Methods In this study, we propose a prediction scoring method that integrates heterogeneous data using a weighted Bayesian method for drug combination prediction. Heterogeneous data refers to different types of data related to drugs, such as chemical, pharmacological, and target profiles. By constructing a multiplex drug similarity network, we formulate new features for drug pairs and propose a novel Bayesian-based integration scheme with the introduction of weights to integrate information from various sources. This method yields support strength scores for drug combinations to assess their potential effectiveness. Results Upon comprehensive comparison with other methods, our method shows superior performance across multiple metrics, including the Area Under the Receiver Operating Characteristic Curve, accuracy, precision, and recall. Furthermore, literature validation shows that many top-ranked drug combinations based on the support strength score, such as goserelin and letrozole, have been experimentally or clinically validated for their effectiveness. Conclusions Our findings have significant clinical and practical implications. This new method enhances the performance of drug combination predictions, enabling effective pre-screening for trials and, thereby, benefiting clinical treatments. Future research should focus on developing new methods for application in various scenarios and for integrating diverse data sources.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-024-05660-3.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-024-05660-3
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	22
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-28
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Combination therapy Prediction scoring method Heterogeneous data Weighted Bayesian method Multiplex drug similarity network Support strength score Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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