ArticleGenome biology2026
scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.Genome biology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cell-cell communication regulates complex biological processes in multicellular systems. Existing scRNA-seq-based methods typically aggregate gene expression by clusters, overlooking within-cluster heterogeneity. We present scComm, a computational framework that infers cell-cell communications between individual cells using supervised contrastive learning. In simulations, scComm outperforms other methods and achieves up to 95% accuracy. Applied to colorectal cancer, it reveals cell-cell communications linked to PD-1 blockade response and tertiary lymphoid structures. In liver cancer, it identifies three novel tumor subtypes and angiogenesis-promoting neutrophil subtypes that have unique tumor microenvironments. scComm enables high-resolution cell-cell communication analysis, uncovering biological insights missed by existing approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.