arXiv:2602.15253v2 Announce Type: replace Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first systematic study of scaling behaviour for masked-reconstruction transformers trained on single-cell RNA sequencing (scRNA-seq) data. Using expression profiles from the CELLxGENE Census, we construct two experimental regimes: a data-rich regime (512 highly variable genes, 200,000 cells) and a data-l
The proliferation of advanced AI architectures in other domains is naturally leading to their application and exploration in biology, where large datasets are becoming increasingly common.
This research extends neural scaling laws, previously confined to language and vision, into biological data, suggesting predictable performance gains akin to those seen in other AI fields.
The understanding of how AI models perform and scale with single-cell genomics data is now becoming clearer, offering a framework for future development and resource allocation in computational biology.
- · AI researchers in biology
- · Biopharmaceutical companies
- · Computational genomics
- · Healthcare sector
- · Traditional bioinformatics methods
- · Small-scale genomics labs
Further investment and research will be directed towards large-scale AI models for single-cell analysis.
The development of new diagnostic and therapeutic approaches could accelerate through highly efficient biological data analysis.
The ability to predictably scale AI in genomics might lead to a more profound, AI-driven understanding of fundamental biological processes and disease mechanisms.
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Read at arXiv cs.LG