SOURCE-LINKED INTELLIGENCE
Towards a knowledge-enhanced single-cell foundation model
Single-cell foundation models (scFMs) increasingly rely on large-scale transcriptomic pretraining, yet expanding pretraining data can yield diminishing gains while substantially increasing computational cost. Our data scaling analyses showed that incorporating biological knowledge, including cell-level text annotation and gene-level regulatory information, provided additional scaling dimension than simply increasing data size. Motivated by this observation, we present scKITE, a simple yet effective scFM that integrates cell-annotation and gene-regulatory supervision into a shared transcriptomi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T03:23:55.000Z
First collected: 2026-09-20T12:21:05.240Z. This is not the publication date.