ArticleGenes2024
TExCNN: Leveraging Pre-Trained Models to Predict Gene Expression from Genomic Sequences.
Article in Genes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- AOP-DRL: A deep representation learning framework for the computational prediction of antioxidant peptides.Computational and structural biotechnology journal · 2025Article
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Authors and funding
5 authors.
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Abstract
BACKGROUND/
objectivesUnderstanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences.
methodsWe introduce TExCNN, a novel framework that integrates the pre-trained models DNABERT and DNABERT-2 to generate word embeddings for DNA sequences. We partitioned the DNA sequences into manageable segments and computed their respective embeddings using the pre-trained models. These embeddings were then utilized as inputs to our deep learning framework, which was based on convolutional neural network.
resultsTExCNN outperformed current state-of-the-art models, achieving an average R
conclusionsOur 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.
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