Background/Objectives: Understanding 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. Methods: We 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. Results: TExCNN outperformed current state-of-the-art models, achieving an average R2 score of 0.622, compared to the 0.596 score achieved by the DeepLncLoc model, which is based on the Word2Vec model and a text convolutional neural network. Furthermore, when the sequence length was extended from 10,500 bp to 50,000 bp, TExCNN achieved an even higher average R2 score of 0.639. The prediction accuracy improved further when additional biological features were incorporated. Conclusions: 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.

Guohao Dong

Yuqian Wu

Lan Huang

Fei Li

Fengfeng Zhou

Genes

GENES (ISSN 2073-4425) 是一本经过同行评审的国际开放获取期刊，为与基因、遗传学和基因组学相关的研究提供了一个高级论坛。它发表评论、研究文章、通讯和技术说明。论文的长度没有限制，我们鼓励科学家尽可能详细地发表他们的结果。

GENES (ISSN 2073-4425) is a peer-reviewed international open-access journal that provides a high-level forum for research related to genes, genetics, and genomics. It publishes comments, research articles, communications, and technical notes. There is no restriction on the length of papers, and we encourage scientists to present their results as comprehensively as possible.

《Genes》（ISSN 2073-4425）是一本经过同行评审的国际开放获取期刊，专注于基因、遗传学和基因组学相关研究。期刊为研究者提供了一个高级论坛，发表内容包括评论、研究文章、通讯和技术说明。论文长度没有限制，我们鼓励科学家尽可能详细地分享他们的研究成果。

Category	Quartile
生物学	3
生物学, 遗传学	3

TExCNN: Leveraging Pre-Trained Models to Predict Gene Expression from Genomic Sequences

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Journal Info

Category	Quartile
GENETICS & HEREDITY	2