TExCNN: Leveraging Pre-Trained Models to Predict Gene Expression from Genomic Sequences
Our experimental results demonstrate that the use of pre-trained models for word embedding generation significantly improves the accuracy of predicting gene expression. The proposed TExCNN pipeline performes optimally with longer DNA sequences and is adaptable for both cell-type-independent and cell-type-dependent predictions.
Authors
- Fengfeng Zhou (ORCID: https://orcid.org/0000-0002-8108-6007)
- Yuqian Wu (ORCID: https://orcid.org/0000-0001-8664-8980)
- Fei Li (ORCID: https://orcid.org/0000-0002-1338-2533)
- Lan Huang (ORCID: https://orcid.org/0000-0003-3233-3777)
- Guangguo Dong (ORCID: https://orcid.org/0000-0001-9439-4423)
Institutions
- Jilin University (CN)
Publication Details
- Journal
- Genes
- Published
- 2024-12-12
- DOI
- https://doi.org/10.3390/genes15121593
- Citations
- 2
- Primary Topic
- RNA and protein synthesis mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- Health Commission of Guizhou Province
- National Natural Science Foundation of China
- People's Government of Jilin Province
- Education Department of Jilin Province
- Fundamental Research Funds for the Central Universities