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CellTune: an integrative software for accurate cell classification in spatial proteomics

Nature Methods Bioinformatics AI Yuval Bussi, Dana Shainshein, Eli Ovits, Sarah Posner, Nofar Azulay, Noa Maimon, Tal Keidar Haran, Raz Ben-Uri, Caitlin Brown, Noam Schuldiner, Eylon Yaniv, David Van Valen, Idan Milo, Ofer Elhanani, Robert Schiemann, Leeat Keren 2026-07-31

TL;DR - CellTune is software for high-precision cell classification in spatial proteomics datasets. It uses human-in-the-loop active learning to integrate expert feedback into the analysis workflow.

  • Targets cell classification in spatial proteomics data.
  • Combines automated analysis with iterative human input.
  • Aims to improve classification accuracy through active learning.
  • The provided abstract does not include quantitative results or benchmarking details.

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