SVEA: an accurate model for structural variation detection using multi-channel image encoding and enhanced AlexNet architecture
This study presents SVEA, a deep learning model incorporating advanced encoding and feature extraction techniques to enhance structural variation prediction. The model demonstrates high accuracy, outperforming existing methods by approximately 4%, while also identifying areas for further optimization.
Authors
- Jijun Tang (ORCID: https://orcid.org/0000-0002-6377-536X)
- Yan Guo (ORCID: https://orcid.org/0000-0001-5252-3960)
- Limin Jiang (ORCID: https://orcid.org/0000-0002-4320-3698)
- Jiawei Li (ORCID: https://orcid.org/0000-0002-8171-478X)
- Tian Qiu (ORCID: https://orcid.org/0009-0001-0286-6188)
Institutions
- University of Miami (US)
- Shenzhen University (CN)
- Southern University of Science and Technology (CN)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-02-22
- DOI
- https://doi.org/10.1186/s12967-025-06213-y
- Citations
- 1
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China