Top 40
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ML / AI methods
Interpretable deep learning and graph methods integrate multiomics and spatial molecular…
Recent papers pair interpretable and uncertainty-aware machine learning with multiomics and spatial data for small-sample and integrative analyses.
Transcriptomics
Neural and graph models recover finer spatial gene expression and tissue domains
Recent methods target spot-level limits in spatial transcriptomics using histology fusion, graph learning, and implicit neural fields.
ML / AI methods
Deep learning biological predictors tested against null and feature-only baselines
Recent preprints and papers test transformer and graph models against null baselines across single-cell, bulk, and genomic prediction tasks.
Structural bio & protein design
Learned models predict protein binding, stability, pathogenicity and flexibility
Recent 2026 preprints and articles extend protein language model and structure features with graph, algebraic and contrastive training layers.
Single-cell
Multi-omic single-cell data resolve dynamic, cell-type-specific gene regulatory networks
Recent methods add trajectory, regulatory priors, and latent TF activity modeling to single-cell regulatory network inference.