Top 40
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Transcriptomics
Computational methods refine spatial gene expression to cell-level resolution
2026 preprints and journal articles extend spatial transcriptomics methods to resolution enhancement, domain identification, and multi-omics integration.
ML / AI methods
Biological priors and architecture choices determine deep-learning gains in genomics
Recent preprints and publications test whether graph, sequence and foundation-model designs add biological value beyond random or feature-only baselines.
Structural bio & protein design
Learned models predict protein binding sites, stability, and mutational effects
Protein language models and graph networks now drive binding-site and binding-energy prediction, with new gains from representation and training design.
ML / AI methods
Deep learning models predict drug response, combinations, and drug-target interactions
Recent publications fuse structural, transcriptomic, and graph representations to improve drug response and binding affinity prediction.
Single-cell
Graph and transformer models advance scRNA-seq cell typing and malignant cell…
Recent 2026 work pairs graph and transformer architectures with scRNA-seq and spatial data for annotation, rare-cell detection, and malignant cell…