DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification
DNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.
Authors
- Ziying Yan (ORCID: https://orcid.org/0000-0001-8210-5567)
- Wenxiang Wang (ORCID: https://orcid.org/0000-0001-7297-3387)
- Changlong Jin (ORCID: https://orcid.org/0000-0001-6943-6708)
- Qian Zhu (ORCID: https://orcid.org/0009-0002-5685-4809)
- Zehua Wang (ORCID: https://orcid.org/0009-0007-2803-9942)
- Minghao Yang (ORCID: https://orcid.org/0000-0001-5397-3576)
Institutions
- Shandong University (CN)
- Weihai Municipal Hospital (CN)
- Beijing Research Institute of Automation for Machinery Industry (China) (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1186/s12859-024-05955-8
- Citations
- 6
- Primary Topic
- Genomics and Phylogenetic Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.40