iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk
Summary
Large language models (LLMs) have demonstrated impressive capabilities in summarization, reasoning, and content generation, yet their inability to directly interpret large-scale omics data has limited their utility in data-driven hypothesis generation-particularly in mechanism discovery that demands the integration and interpretation of multimodal datasets, heterogeneous models, and deep domain expertise. Conversely, traditional computational algorithms excel at quantitative analysis of om
Content
# iS2C2: a cointelligent platform for mechanistic discovery of disease cellular crosstalk
*Published: 2026 May 11*
Large language models (LLMs) have demonstrated impressive capabilities in
summarization, reasoning, and content generation, yet their inability to
directly interpret large-scale omics data has limited their utility in
data-driven hypothesis generation-particularly in mechanism discovery that
demands the integration and interpretation of multimodal datasets, heterogeneous
models, and deep domain expertise. Conversely, traditional computational
algorithms excel at quantitative analysis of omics data but often rely heavily
on labor-intensive, expert-driven interpretation to extract biologically
meaningful insights. Here, we introduce (cointelligent single-cell spatial
cell‒cell communication: iS2C2), a novel cointelligent platform that synergizes
mathematically rigorous computational algorithms with the contextual reasoning
capabilities of LLMs to automatically generate biologically interpretable
hypotheses from single-cell RNA-seq and spatial transcriptomics data. The iS2C2
platform incorporates a transparent and reproducible cell-cell communication
analysis pipeline built upon mathematically rigorous algorithms designed to
enhance interpretability for integration with LLMs that contextualize
algorithmic outputs or predictions using domain-specific knowledge and
literature-derived evidence. When applied to Alzheimer's disease and cancer
datasets, iS2C2 generated accurate, reproducible, and expert-validated
hypotheses, unveiling previously unrecognized signaling pathways and mechanistic
insights in disease microenvironments. This cointelligent approach bridges the
gap between structured computational analysis and generative reasoning,
heralding a paradigm shift toward fully automated, interpretable biological
discovery and advancing the frontiers of next-generation precision medicine and
systems biology.
DOI: 10.1038/s41392-026-02691-8