Top 40
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Single-cell
Learned models fuse histology, transcriptomic, and chromatin data to infer biology
Recent preprints and papers pair interpretable or foundation-model components with transcriptomic, spatial, and multiomic single-cell data.
Single-cell
Perturbation models match expression yet miss intervention mechanisms and geometry
Recent preprints and papers test whether single-cell perturbation models capture mechanism rather than expression similarity alone.
Structural bio & protein design
Force fields and generative models target disordered proteins and RNA condensates
Recent preprints refine force fields and generative models for disordered proteins, condensates, and RNA-protein conformational ensembles.
ML / AI methods
Deep learning models predict RNA processing, stability and regulatory interactions
Benchmarks and interpretable models now test RNA splicing, 3'UTR stability and sRNA-mRNA prediction against large-scale experimental data.
Structural bio & protein design
Benchmark design determines which structure and affinity prediction gains hold up
Fresh preprints test co-folding, TCR-pMHC modeling, and water placement on curated or held-out sets rather than relying on headline architecture gains.