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Decoding intercellular activities for more than 1,000 secreted proteins

Research Bioinformatics AI

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TL;DR - SecAct is a computational framework for inferring signaling activity among 1,170 human secreted proteins from transcriptomic data. It enables systematic study of protein-mediated intercellular communication across tissues and diseases.

  • Supports spatial, single-cell, and bulk transcriptomic datasets.
  • Focuses on secreted-protein-mediated signaling between cells.
  • Can analyze communication patterns across tissue and disease cohorts.

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Decoding intercellular activities for more than 1,000 secreted proteins

Nature Methods 2026-07-30 doi:10.1038/s41592-026-03185-9
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-28 14:33:42.573442 UTC

TL;DR - SecAct is a computational framework for inferring signaling activity among 1,170 human secreted proteins from transcriptomic data. It enables systematic study of protein-mediated intercellular communication across tissues and diseases.

  • Supports spatial, single-cell, and bulk transcriptomic datasets.
  • Focuses on secreted-protein-mediated signaling between cells.
  • Can analyze communication patterns across tissue and disease cohorts.
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