Top 40
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Transcriptomics
Learned models infer cell structure in single-cell and spatial transcriptomics
Recent preprints and publications apply learned architectures to multimodal integration, spatial domains, and histology-based expression prediction.
Single-cell
Standardized benchmarks probe generalization of ML models in perturbation biology
Large-scale public assays and leakage audits are testing whether predictive models generalize beyond their training contexts.
Epigenomics
Computational methods extend methylation and expression analysis across platforms and…
Recent preprints and papers introduce statistical and machine-learning tools for methylation, expression, and off-target artifacts in genomic assays.
ML / AI methods
Learned models predict variant effects, regulatory activity and peptide-TCR specificity
Deep learning and language models now target variant pathogenicity, regulatory elements and TCR-peptide specificity across recent preprints and papers.
Systems biology
Adaptive feedback and modularity in computational models of neural and metabolic systems
Models now embed adaptive regulation and learnable dynamics to test how neural and metabolic systems adapt to perturbation and temporal tasks.