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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Transformer-based representation learning for robust gene expression modeling and cancer prognosis.

Shuai Jiang¹, Saeed Hassanpour^2,3,4

¹Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.

Scientific Reports

|October 29, 2025

Summary

GexBERT, a new transformer model, effectively analyzes gene expression data, even with missing values. It improves cancer classification, survival prediction, and data imputation for better biological insights.

Keywords:

Cancer prognosis Deep learning in genomics Gene expression modeling Representation learning Transformer networks

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Transformer models excel in NLP and vision but struggle with sparse, high-dimensional gene expression data.
Challenges include data sparsity, high dimensionality, and missing values in transcriptomic profiles.

Purpose of the Study:

Introduce GexBERT, a transformer-based framework for robust gene expression representation learning.
Address limitations in applying deep learning to gene expression analysis.

Main Methods:

GexBERT utilizes an encoder-decoder architecture pretrained on large-scale transcriptomic data.
A masking and restoration objective captures gene co-expression relationships.
Evaluated on pan-cancer classification, survival prediction, and missing value imputation.

Main Results:

Achieved state-of-the-art classification accuracy using limited gene subsets.
Enhanced cancer-specific survival prediction by restoring prognostic gene expression.
Outperformed traditional methods in missing value imputation under high missingness.

Conclusions:

GexBERT offers a scalable and effective tool for gene expression modeling.
Demonstrates translational potential for gene expression analysis with limited or incomplete data.