UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.

Yaojia Chen, Yumeng Zhang, Mengting Niu, Jiacheng Wang, Zhonghao Ren, Quan Zou, Jiangning Song, Ximei Luo

Abstract read

In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Yaojia ChenThe Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.

Yumeng ZhangDepartment of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, 3800, Australia.

Mengting NiuInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Jiacheng WangInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Zhonghao RenCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Jiangning SongDepartment of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, 3800, Australia. Jiangning.Song@monash.edu.

Ximei LuoInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. luoximei@uestc.edu.cn.

Funding

National Natural Science Foundation of China No. 62302341National Natural Science Foundation of China No.62425107National Natural Science Foundation of China No. 62473268

6 · The paper itself

Abstract

Drug combinations can improve cancer therapy by boosting efficacy, limiting dose-related toxicity, and delaying resistance. We present UniSyn, an interpretable multi-modal deep learning framework that transfers knowledge from monotherapy responses to enhance drug-synergy prediction. Through hybrid attention-based integration of drug and cell-line features, UniSyn supports multi-task learning and yields mechanistic insights. It generalizes robustly to unseen drug pairs and cell types, maintaining consistent performance across multiple synergy scoring metrics. Applied at scale to tumor cell lines, UniSyn captures context-specific synergy signals and prioritizes therapeutic combinations with translational potential.

Indexed as

Antineoplastic AgentsDeep LearningDrug SynergismNeoplasmsCell Line, TumorHumansAntineoplastic AgentsDeep learningDrug synergy predictionKnowledge transferMechanistic interpretabilityMulti-task learning

Identifiers

PMID41639919

DOI10.1186/s13059-026-03972-9

PMCPMC12964627

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LicenceCC BY-NC-ND

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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 OpenQuestion graph.