ArticleInterdisciplinary sciences, computational life sciences2021
An In Silico Method for Predicting Drug Synergy Based on Multitask Learning.
Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein-protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug-target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug-target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.
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Registered trials
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