Labs & Groups (1)
Models (6)
Structure-aware protein language model using structure-guided masking and a causal objective for variant effect prediction and protein discovery.
Hierarchical DNA foundation model that co-trains a dynamic token-merging tokenizer with latent Transformers to match genomic information density.
Scaling-law study of protein language models identifying compute-optimal training for causal and masked objectives on 939 million protein sequences.
Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.
Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.
Asymmetric encoder-decoder transformer for single-cell RNA-seq that encodes only non-zero genes, cutting FLOPs 10-100x versus standard transformers.