Top 40
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Single-cell
Learned models infer regulation, dynamics and cell identity from single-cell omics data
Deep generative and graph models now disentangle regulatory, cell-cycle and dynamic structure in single-cell and spatial transcriptomic data.
Transcriptomics
Graph and implicit models decode spatial niches, imputed expression and histology links
Spatial transcriptomics now pairs with histology and foundation models, pushing graph methods from domain clustering toward imputation and 3D reconstruction.
ML / AI methods
Omics machine learning models are tested for generalization beyond training settings
Multi-population benchmarks and generalization tests are now appearing alongside method papers for omics machine learning.
Tools & infrastructure
Modular Snakemake workflows integrate SNV, indel, STR and structural variant detection
Recent software releases package reproducible pipelines for trio de novo variant calling and mutational signature extraction from optical mapping data.
ML / AI methods
Structure-aware deep learning extends binding, epitope, and peptide prediction and design
Structure-aware GNNs and fine-tuned protein models now target druggability, degradability, epitopes, stability, and peptide design.