Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery.
Summary
An interpretable transformer-based pipeline (MIST + single-cell FastShap) explained 70–82% of variance in RBC, WBC, and PLT counts from single-cell morphology data, far surpassing conventional methods. It revealed co-regulatory hematologic mechanisms and identified a WBC-derived biomarker, Down Shift, that augments diagnostic associations with sepsis.
Key Findings
- MIST explained 70–82% of variance in RBC, WBC, and PLT counts versus 5–20% with current approaches.
- Interpretability maps revealed co-regulatory crosstalk among hematologic populations and identified granular subgroups.
- A single-WBC biomarker, Down Shift, complemented inflammation markers and strengthened diagnostic associations with sepsis and other diseases.
Clinical Implications
If validated, Down Shift could complement existing inflammatory markers to improve early sepsis detection using routinely collected hematology data without new assays.
Why It Matters
Provides a generalizable, interpretable AI framework and a rational biomarker discovery process from routine single-cell hematology, linking to sepsis diagnostics and systems biology.
Limitations
- Preprint without peer review; external prospective validation not reported.
- Dataset size and multi-center generalizability are not specified in the abstract.
Future Directions
Prospective multi-center validation of Down Shift; evaluation of clinical decision thresholds; integration with sepsis early warning systems and comparison against established biomarkers.
Study Information
- Study Type
- Cohort
- Research Domain
- Pathophysiology
- Evidence Level
- III - Retrospective model development using routine clinical single-cell data with internal validation; no peer-reviewed external validation reported.
- Study Design
- OTHER