Health AI Central
Peer-Reviewed Publication
Sci Rep2025;15(1):37581.October 28, 2025Journal Article

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

Shuai Jiang1, Saeed Hassanpour2,3,4
1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA.
2Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. Saeed.Hassanpour@dartmouth.edu.
3Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, 03755, USA. Saeed.Hassanpour@dartmouth.edu.
4Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA. Saeed.Hassanpour@dartmouth.edu.
PubMed PMC DOI PDF

Abstract

Transformer models have achieved remarkable success in natural language and vision tasks, but their application to gene expression analysis remains limited due to data sparsity, high dimensionality, and missing values. We present GexBERT, a transformer-based encoder-decoder framework for robust representation learning of gene expression data. GexBERT learns context-aware gene embeddings by pretrai…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.