May 8, 2024, 4:42 a.m. | Andac Demir, Elizaveta Solovyeva, James Boylan, Mei Xiao, Fabrizio Serluca, Sebastian Hoersch, Jeremy Jenkins, Murthy Devarakonda, Bulent Kiziltan

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.03726v1 Announce Type: cross
Abstract: Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction, it remains to be seen if a simpler model could achieve comparable or better results, especially with limited data. This is important, as the quantity and quality of single-cell data typically fall short of the standards in textual data used for training LLMs. Single-cell sequencing often suffers from …

abstract arxiv classification clustering cs.lg foundation gene llms manifold modeling performance prediction q-bio.gn show transport type while

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