Top 40
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Single-cell
Learned models reconstruct spatial and temporal single-cell transcriptomic profiles
Generative and graph-based learning is now applied to spatial imputation, trajectory inference, and counterfactual editing of single-cell data.
Structural bio & protein design
Learned models predict and design protein properties; benchmarks expose limits
Pretrained protein and DNA language models are now benchmarked directly against baselines across structure, stability, binding, and design tasks.
Epigenomics
Computational models infer chromatin loops, states and regulatory elements
Large epigenome compendia and paired single-cell multiomics now enable quantitative models of regulatory architecture.
Transcriptomics
Computational methods for spatial, single-cell, and multi-omics gene analysis
Recent papers and preprints package gene set, network, and spatial transcriptomics methods into benchmarked, reproducible computational workflows.
ML / AI methods
Biological priors are being built into transcriptomic and deconvolution models
Recent preprints and papers embed gene regulatory, pathway, and spatial graph priors into models to keep predictions tied to biological structure.