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Related Experiment Video

Updated: Jul 3, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

BulkFormer: A large-scale foundation model for bulk transcriptomes.

Boming Kang¹, Rui Fan¹, Meizheng Yi²

¹Department of Biomedical Informatics, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd., Beijing 100191, China.

|July 1, 2026

Summary

This summary is machine-generated.

Foundation models are advancing transcriptome analysis. BulkFormer, a new model for bulk RNA sequencing data, outperforms existing methods and reduces training costs.

Keywords:

deep learning foundation models human bulk transcriptomes

Related Experiment Videos

Last Updated: Jul 3, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Foundation models are revolutionizing transcriptome analysis.
Current models often use sparse single-cell RNA sequencing (scRNA-seq) data, limiting analysis to ~3,000 genes.
Bulk RNA sequencing (bulk RNA-seq) profiles more genes (~16,000) and is crucial for clinical and tissue-level studies.

Purpose of the Study:

To develop a foundation model specifically for bulk transcriptome analysis.
To address the limitations of scRNA-seq based models for bulk data.
To create a robust framework for bulk RNA-seq data modeling.

Main Methods:

Introduction of BulkFormer, a foundation model with ~150 million parameters.
Pretraining on 581,503 human bulk RNA-seq profiles, covering 20,010 protein-coding genes.
Utilizing a hybrid encoder combining a graph neural network and a Performer module.

Main Results:

BulkFormer demonstrated superior performance across five downstream tasks compared to existing scRNA-seq foundation models.
Achieved high performance with substantially lower training costs.
Highlighted the importance of pretraining data modality for foundation model efficacy.

Conclusions:

BulkFormer provides an effective framework for bulk transcriptome analysis.
The study underscores the impact of data modality on foundation model performance.
Establishes BulkFormer as a valuable tool for clinical and tissue-level transcriptomic studies.