Simple models outperform single-cell foundation models on downstream benchmarks

bioRxiv 预印本 · 2026-11-10 · 预印本

Single-cell RNA sequencing (scRNA-seq) data exhibit strong and reproducible statistical structure. This has motivated the development of large-scale foundation models, such as TranscriptFormer, that use transformer-based architectures to learn a generative model for gene expression by embedding genes into a latent vector space. These embeddings have been used to obtain state-of-the-art (SOTA) performance on downstrea…

（中文解读待补写）

🔗 打开原文

Souza, H., Mehta, P.

本文摘自《每日前沿研究简报 · 2026-11-12》「生物信息 / 组学（单细胞·差异表达）」。本内容仅用于研究信息整理与科普交流，不构成医疗建议。