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SwitchClass distinguishes baseline- and perturbation-aligned features across intermediate molecular states
Di Xiao1, Siqu Long2, Pengyi Yang3
1Computational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia; Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Cell Reports Methods
|August 12, 2026
Summary
SwitchClass identifies molecular changes in biological systems by classifying intermediate states. This framework reveals proteins and signaling pathways involved in partial recovery or persistent dysregulation across various conditions.
Area of Science:
- Systems biology
- Computational biology
- Bioinformatics
Background:
- Biological systems under perturbation often display intermediate molecular states.
- Understanding molecular resilience and maladaptive persistence requires capturing these complex dynamic patterns.
Purpose of the Study:
- Introduce SwitchClass, a novel label-switch classification framework.
- Distinguish features with intermediate-state profiles aligning to baseline or perturbed conditions.
- Quantify feature alignment across biological states using a directional importance score (δ).
Main Methods:
- Developed a dual classifier with inverted labels for training.
- Applied SwitchClass to analyze colorectal cancer proteomics data.
- Utilized SwitchClass on dietary perturbation phosphoproteomics and COVID-19 single-cell transcriptomes.
Main Results:
- Identified proteins that normalize post-therapy versus those remaining dysregulated in colorectal cancer.
- Uncovered phosphorylation sites linked to incomplete insulin signaling restoration in dietary studies.
- Detected cell-type-specific transcripts indicating inflammatory activity resolution or persistence in COVID-19 patients.
Conclusions:
- SwitchClass provides an interpretable framework for mapping directional molecular changes in systems with intermediate states.
- Demonstrated SwitchClass's utility in diverse biological contexts including cancer, metabolic studies, and infectious disease.
- Highlights the potential for SwitchClass in understanding complex biological system dynamics.
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