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New autoregressive transformer model for single-cell gene expression generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel autoregressive transformer model designed for generating single-cell gene expression vectors. This model, which incorporates a learned quantized variational auto-encoder tokenizer, is trained using a cross-entropy loss. The study evaluates the biological fidelity and scaling behavior of the model, identifying a compute-optimal frontier for single-cell foundation models. The pretrained model is also discussed as a potential tool for finetuning for perturbation response prediction. AI

IMPACT Introduces a new foundation model for single-cell data, potentially advancing biological research and drug discovery.

RANK_REASON Academic paper detailing a new model architecture and its scaling properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New autoregressive transformer model for single-cell gene expression generation

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Academic paper detailing a new model architecture and its scaling properties. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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52 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aleksandr Sharipov, Yusif Mukhtarov, Igor Molybog ·

    Scaling an Autoregressive Transformer for Single-Cell Generation

    arXiv:2608.02961v1 Announce Type: cross Abstract: We study a self-supervised generation task for single-cell gene expression vectors: given a set of vectors from a cell type, we aim to generate additional gene expression vectors of that cell type. For this task we characterize bo…

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