Top 40
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ML / AI methods
Learned models infer regulatory logic and cell states from single-cell and imaging data
Interpretable pretrained and graph-based frameworks are now reported across scATAC-seq, scRNA-seq, spatial transcriptomics, flow cytometry and cell painting.
Single-cell
Computational methods reinterpret single-cell, protein, and metabolite measurements
Omics analyses increasingly model confounding layers such as missing controls, localization, and spatial context explicitly.
ML / AI methods
Language models and genome resources map bacterial immunity and phage-host metabolism
Protein and genomic language models and genome-scale models now predict antiphage defenses and host metabolic hijacking directly from sequence and public data.
Epigenomics
Multiomic and AI-based methods resolve enhancer wiring and histone state landmarks
Recent work pairs single-cell multiomics, AI footprinting and reprocessed ChIP-seq atlases to link chromatin state with transcription.
Single-cell
Learned models infer single-cell state trajectories and gene regulatory networks
Learned dynamical and generative models now connect snapshot, imaging, and temporal single-cell data to predicted gene expression and state transitions.