AGEP_TWAS: A Deep Learning-based Framework for Predicting Gene Expression Levels in Tissues

Wang S. Zhou Z. Li C. Cai Y. Wang J. Zhang Z. Li R. Niyazbekova Z. Jiang Y. Liu Q.
2026 Institute of Electrical and Electronics Engineers Inc.

IEEE Transactions on Computational Biology and Bioinformatics
2026

Accurate prediction of gene expression levels across different tissues is of great significance in understanding the functional roles of genes in various biological processes and assisting in transcriptome-wide association studies (TWAS). Traditional methods rely on the construction of a prediction model for each gene in a specific tissue, which is time-consuming and inefficient when dealing with numerous genes or tissues. In addition, current approaches do not consider missing single nucleotide polymorphisms (SNPs) in the training population. These SNPs significantly affect gene expression levels and in turn limit the predictive capability of these approaches in new samples. Recent research indicates that by identifying a specific group of core genes (known as landmark genes) that accurately reflect the cellular states of samples across different experimental conditions, it is possible to predict the expression levels of other genes in the genome. In light of this, we propose AGEP_TWAS (Adaptive Gene Expression Predictor for TWAS), a gene expression prediction method that utilizes a dense connection network, adaptive activation functions, and parameter pruning strategies within a nonlinear feature extraction computational framework. AGEP_TWAS leverages landmark genes within a tissue to predict the expression levels of other genes that are challenging to predict using traditional methods. Results on the human GEO expression dataset demonstrate that AGEP_TWAS achieved a mean squared error (MSE) of 0.1821 and a Pearson correlation coefficient (PCC) of 0.9004, outperforming existing state-of-the-art prediction models. Additionally, when applied to the CattleGTEx dataset to infer gene expression levels across different tissues in cattle, AGEP_TWAS exhibited superior predictive performance compared to existing methods. A TWAS on milk production traits in cattle highlights the practical utility of AGEP_TWAS, with six identified significant genes already reported in scientific literature to be associated with milk production traits.

Deep Learning , Gene expression prediction , landmark genes , TWAS

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College of Information Engineering, Northwest A&F University, Yangling, 712100, China
Monash University, Department of Biochemistry and Molecular Biology, Melbourne, 3800, VIC, Australia
College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
Kazakh national agrarian research university, Reference center for safety and quality of agricultural products, Almaty, Kazakhstan

College of Information Engineering
Monash University
College of Animal Science and Technology
Kazakh national agrarian research university

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026