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ML / AI methods
ML models fuse omics, imaging and clinical data for cancer prediction and subtyping
Learned models across omics, imaging and graph representations now target recurrence, immunotherapy response, survival and subtype discrimination.
Single-cell
LLM benchmarks probe scRNA-seq cell-type annotation accuracy and ensemble strategies
Recent benchmarks test LLMs on scRNA-seq cell-type annotation and SRA metadata curation, alongside non-LLM reference-mapping methods.
ML / AI methods
Protein language model embeddings drive diverse sequence-based prediction frameworks
Recent published frameworks pair pretrained protein language model embeddings with structure graphs, attention, and recurrent or convolutional architectures.
ML / AI methods
Deep learning models predict RNA, DNA and protein modification and binding sites from…
Recent publications apply attention networks and pretrained language models to predict RNA, DNA and protein modification and binding sites.
Transcriptomics
Graph models advance spatial domain, gene regulatory, and imputation analysis
Graph-based deep learning now spans multislice, spatiotemporal, histology-to-expression, and denoising tasks in spatial omics.