BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models

• URL: http://arxiv.org/abs/2506.14861v1
• Date: Tue, 17 Jun 2025 15:40:08 GMT
• Title: BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation Models
• Authors: Bharath Dandala, Michael M. Danziger, Ella Barkan, Tanwi Biswas, Viatcheslav Gurev, Jianying Hu, Matthew Madgwick, Akira Koseki, Tal Kozlovski, Michal Rosen-Zvi, Yishai Shimoni, Ching-Huei Tsou,
• Abstract summary: We present BMFM-RNA, an open-source, modular software package that unifies diverse TFM pretraining and fine-tuning objectives.<n>We introduce a novel training objective, whole cell expression decoder (WCED), which captures global expression patterns using an autoencoder-like CLS bottleneck representation.<n>We show that WCED-based models achieve performance that matches or exceeds state-of-the-art approaches like scGPT.
• Score: 1.1906923449182683
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transcriptomic foundation models (TFMs) have recently emerged as powerful tools for analyzing gene expression in cells and tissues, supporting key tasks such as cell-type annotation, batch correction, and perturbation prediction. However, the diversity of model implementations and training strategies across recent TFMs, though promising, makes it challenging to isolate the contribution of individual design choices or evaluate their potential synergies. This hinders the field's ability to converge on best practices and limits the reproducibility of insights across studies. We present BMFM-RNA, an open-source, modular software package that unifies diverse TFM pretraining and fine-tuning objectives within a single framework. Leveraging this capability, we introduce a novel training objective, whole cell expression decoder (WCED), which captures global expression patterns using an autoencoder-like CLS bottleneck representation. In this paper, we describe the framework, supported input representations, and training objectives. We evaluated four model checkpoints pretrained on CELLxGENE using combinations of masked language modeling (MLM), WCED and multitask learning. Using the benchmarking capabilities of BMFM-RNA, we show that WCED-based models achieve performance that matches or exceeds state-of-the-art approaches like scGPT across more than a dozen datasets in both zero-shot and fine-tuning tasks. BMFM-RNA, available as part of the biomed-multi-omics project ( https://github.com/BiomedSciAI/biomed-multi-omic ), offers a reproducible foundation for systematic benchmarking and community-driven exploration of optimal TFM training strategies, enabling the development of more effective tools to leverage the latest advances in AI for understanding cell biology.

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