ArticleBriefings in bioinformatics2026
Deconformer: efficient cell-type composition profiling of cfRNA across diverse cell types.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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17 authors.
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Abstract
Resolving cell-type contributions in cell-free RNA (cfRNA) profiles is important for liquid-biopsy research, but profiling many potential cellular sources remains computationally challenging. Here, we present Deconformer, a biologically informed deep-learning framework that integrates pathway knowledge into a Transformer architecture to estimate relative cfRNA contributions across 60 major human cell types. In systematic benchmarks using cross-platform simulations selected for similarity to real plasma cfRNA, Deconformer showed improved robustness from pathway-informed modeling and achieved competitive accuracy with substantially lower computational cost than established deconvolution methods. Applied to real cfRNA cohorts, it recapitulated known gestational trajectories and detected disease-associated composition shifts, supporting its use as a practical framework for large-scale cfRNA profiling.
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