Evidence map›Paper›PMID 41269282›Full record

ArticleBriefings in bioinformatics2025

BridgeSyn: a bridging fusion framework for drug combination synergy prediction.

Qingyu Wang, Suwan Mao, Xiaoyan Liu, Quan Zou, Qing Liu, Xi Su, Junjie Wang, Wen Gao, Ximei Luo

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Qingyu WangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, 211166 Jiangsu, China.
Suwan MaoDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, 211166 Jiangsu, China.
Xiaoyan LiuFaculty of Computing, Harbin Institute of Technology, 92 West Dazhi Street, Nangang District, Harbin, 150001 Heilongjiang, China.
Quan ZouYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Kecheng District, Quzhou, 324000 Zhejiang, China.
Qing LiuDepartment of Anesthesiology, Hospital (T.C.M) Affiliated To Southwest Medical University, 182 Chunhui Road, Longmatan District, Luzhou, 646000 Sichuan, China.
Xi SuThe Affiliated Foshan Women and Children Hospital, Guangdong Medical University, 11 Renmin West Road, Chancheng District, Foshan, 528000 Guangdong, China.
Junjie WangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing, 211166 Jiangsu, China.
Wen GaoDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Gulou District, Nanjing, 210029 Jiangsu, China.
Ximei LuoInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-tech Zone, Chengdu, 611731 Sichuan, China.

Funding

Foshan Birth Defects Prevention and Control Engineering Technology Research Center 2420001000293Key Science and Technology Research Project of Foshan 2120001008276National Natural Science Foundation of China 12202211National Natural Science Foundation of China 32270786National Natural Science Foundation of China 62102191National Natural Science Foundation of China 62271174National Natural Science Foundation of China 62371403Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0531800
6 · The paper itself

Abstract

Drug combination is a promising therapeutic strategy for complex diseases. However, only a small fraction of potential drug combinations exhibit true synergistic effects, making the prediction of drug synergy a critical yet challenging task. In this study, we propose BridgeSyn, a novel bridge fusion framework for drug synergy prediction. BridgeSyn leverages the knowledge from pretrained biological language models to enrich both drug compound and cell line representations. We introduce a bridging fusion mechanism that employs a set of shared latent tokens derived from global features, serving as a semantic interface to effectively fuse the representations of drug pairs and cell lines. By combining biological prior knowledge with this fusion strategy, BridgeSyn can capture complex biological interactions and achieve superior prediction results. Extensive experiments on two public datasets demonstrate that BridgeSyn consistently outperforms existing computation methods.

Indexed as

Computational BiologyDrug SynergismSoftwareAlgorithmsDrug CombinationsHumansDrug Combinationsbridging fusion mechanismcombination therapydrug synergy predictionpretrained models

Identifiers

PMID41269282
PMCPMC12636530

What Socratic holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.