Top 40
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Single-cell
Deep learning links single-cell morphology, imaging and spatial transcriptomic profiles
Computational pipelines now integrate imaging, histology, morphology and multi-omics data to resolve single-cell spatial states across tissues.
ML / AI methods
Learned generative models are now used to design and interrogate protein sequences
Recent preprints couple diffusion, autoregressive, and RL generators with structure predictors to target antibodies, peptides, and binders.
ML / AI methods
Shared embeddings fuse tissue images, spatial transcriptomes and molecular profiles
Recent preprints and publications apply contrastive, graph and self-supervised learning to joint image, transcriptomic and molecular data.
Structural bio & protein design
Protein and nucleic acid design tools benchmarked for fitness and specificity
Sequence design pipelines are now evaluated at increasing mutational distance and across nucleic acid and RNA codon targets.
Systems biology
Dynamical models separate measurement noise from growth and metabolic variability
Recent preprints and one published study model measurement noise explicitly alongside biological dynamics in time-series and snapshot data.