A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction
This model demonstrated the superior predictive performance and strong generalization capabilities compared to other models. The EGP Hybrid-ML model proposed in this paper has broad application prospects in bioinformatics, chemical information, and pharmaceutical information. The codes, architectures, parameters, and datasets of the proposed model are available free of charge at GitHub ( https://github.com/gnnumsli/EGP-Hybrid-ML ).
Authors
- Fu‐An Wu (ORCID: https://orcid.org/0000-0001-8887-3663)
- Mengshan Li (ORCID: https://orcid.org/0000-0003-4175-088X)
- Wu Yan (ORCID: https://orcid.org/0000-0002-0488-6967)
- Sheng Sheng (ORCID: https://orcid.org/0000-0003-2764-8125)
- Jun Wang (ORCID: https://orcid.org/0000-0003-2569-1461)
- Li Tan (ORCID: https://orcid.org/0000-0002-1199-9046)
- Weihong Zhou
- Xie Xiao-jun
- Fujun Yu (ORCID: https://orcid.org/0000-0001-5321-7228)
Institutions
- Jiangsu University (CN)
- Jiangsu University of Science and Technology (CN)
- Gannan Normal University (CN)
- Shanghai Electric (China) (CN)
- Chinese Academy of Agricultural Sciences (CN)
- Sericultural Research Institute (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1186/s12915-025-02209-8
- Citations
- 1
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China