All Competitors

Universal foundation model that jointly generates diagnostic text and segments the corresponding targets across ten biomedical imaging modalities.

Generative single-cell foundation model trained on 112 million cells from 12 species, autoregressively modeling gene identities and expression counts.

Single-cell foundation model inferring context-specific protein-protein interactions from cancer transcriptomes via a variational graph autoencoder.

Codon-resolution language models trained on 130 million coding sequences from 20,000 species, learning codon rules for translation and mRNA stability.

Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.

Single-cell foundation model pre-trained on 50 million cells that infers cell-specific gene regulatory networks from transformer attention matrices.

Sparse mixture-of-experts autoregressive protein language model family pretrained on 1.5 trillion amino acid tokens with compute-optimal scaling.

3D MRI organ segmentation foundation model built on Swin-UNETR and trained on the UKBOB whole-body dataset covering 72 organs and skeletal structures.

De novo protein design from natural language: a 16B-parameter framework turning text descriptions into sequences via structure-conditioned generation.

Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.

265M-parameter genomic foundation model whose BioToken scheme folds variants and region annotations into the tokens for variant effect prediction.

Open therapeutics foundation models from Google, built on Gemma-2, for drug-discovery property prediction and conversational reasoning.

Protein dynamics model predicting per-residue probability of microsecond-millisecond conformational exchange from sequence or structure.

Genomic language models with disentangled attention, pretrained on prokaryotic and eukaryotic genomes for sequence classification and variant effects.

Histopathology foundation model pretrained with BEiT masked image modeling on 11M+ tissue image tiles for cancer diagnosis and survival prediction.

Single-cell RNA-seq foundation models combining masked modeling with ontology supervision to classify cell states across unseen donors and diseases.

Universal ultrasound segmentation foundation model adapting the Segment Anything Model to eight anatomical regions in a single promptable network.

Whole-brain axon and soma segmentation foundation model and registration pipeline for developmental connectomics, with no per-stage retraining.

Single-cell DNA methylation foundation model capturing genome-wide CpG dependencies in whole-genome bisulfite sequencing across tissues and species.

Histology-anchored framework pairing an H&E foundation model with a cellular hypergraph to predict single-cell multi-omics from tissue images.

Genomic foundation model trained on 9.3 trillion DNA base pairs across all domains of life, with 40B parameters and a 1-million-token context.

Vision-language foundation model for fetal ultrasound, pretrained on 210,035 image-text pairs for plane classification, biometry, and segmentation.

Chest X-ray vision-language model that drafts the findings section of a radiology report, at 7B parameters small enough to run on a single GPU.

ECG foundation model that treats heartbeats as words and rhythm strips as sentences, using heartbeat-level tokenization for diagnostic classification.