A multimodal framework for comprehensive driver variant prediction in cancer
ModVAR integrates sequence, structure, and cancer omics signals to aid driver-variant discovery. It provides robust performance across tasks, supports hypothesis generation and target discovery, and supplies a large-scale resource that advances cancer research and personalized therapy.
Authors
- Yingzhuo Wang
- Hai Yang (ORCID: https://orcid.org/0000-0002-1161-4337)
- Yijia Chen (ORCID: https://orcid.org/0000-0002-9295-5028)
- Tianyi Zhou (ORCID: https://orcid.org/0000-0001-5348-0632)
- Dongdong Li (ORCID: https://orcid.org/0000-0002-1880-8054)
- Qian Zhang (ORCID: https://orcid.org/0000-0001-9784-5787)
- Zhe Wang (ORCID: https://orcid.org/0000-0002-3759-2041)
- Ting Xiao (ORCID: https://orcid.org/0000-0003-3155-7664)
- Qin Zhou
- Jing Zhang (ORCID: https://orcid.org/0009-0001-5370-0014)
Institutions
- East China University of Science and Technology (CN)
- University of Michigan (US)
- Imperial College London (GB)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1038/s43856-025-01209-7
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China