A weighted Bayesian integration method for predicting drug combination using heterogeneous data.
Combination therapy
Heterogeneous data
Multiplex drug similarity network
Prediction scoring method
Support strength score
Weighted Bayesian method
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
20
05
2024
accepted:
04
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 39342319
doi: 10.1186/s12967-024-05660-3
pii: 10.1186/s12967-024-05660-3
doi:
Substances chimiques
Drug Combinations
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
873Subventions
Organisme : National Key R&D Program of China
ID : 2023YFF1205101
Informations de copyright
© 2024. The Author(s).
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