All Competitors

Conditions a pretrained DNA sequence embedding on CpG methylation to predict gene regulation across cell types and alleles, generalizing zero-shot to imprinting, X-inactivation, and accessibility.

Pathology vision foundation model adapting DINOv3-style self-supervised learning to whole-slide histopathology across continuous magnifications and scales.

A 120M-parameter genomic foundation model that learns adaptive DNA token boundaries via H-Net-style dynamic chunking instead of fixed k-mer or byte-pair tokenization.

Multimodal foundation model pretrained on 1.76 billion paired histology-spatial transcriptomics spots, linking whole-slide images to spatial molecular programs.

A graph foundation model for fMRI brain networks, pretrained across 27 datasets with graph and language prompts for zero/few-shot generalization to unseen disorders.

A hierarchical three-stage cascade that generates chemically modified and macrocyclic peptides in HELM notation, supporting de novo design and constrained infilling.

Protein pretraining framework that learns representations directly from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

Self-supervised foundation model that learns reusable representations of cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types.

A self-supervised masked autoencoder for RNA-seq count data, pretrained on 1.4M public samples to learn transferable transcriptomic representations without per-dataset re-training.

A dual-stream self-supervised foundation model for continuous glucose monitoring data, separating slow physiological state from transient glucose events.

A cell world model pretrained on a 2.4M-cell mouse embryonic atlas that predicts one-step transcriptional state transitions and transfers to perturbation prediction.

Geometry-first generative framework that reconstructs single-cell spatial coordinates by integrating scRNA-seq with spatial transcriptomics, without cell-type labels.

A 700M-parameter DNA language model pretrained on the rice pangenome, built as a reusable foundation model for crop genomics and molecular breeding.

A subspecialty lung-pathology foundation model, fine-tuned from Virchow2 and prospectively validated across 32 clinical tasks spanning the lung diagnostic workflow.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

Combines the ProtT5-XL protein language model with protein-specific evolutionary constraints to predict mutational effects on stability, binding, and epistasis—largely zero-shot.

A 4.7B-parameter Mixture-of-Experts genomic foundation model pretrained on ~1.2 trillion nucleotide tokens from human-associated microbial genomes.

An RNA language model trained by self-supervised masked-nucleotide prediction on ~50,000 IRES sequences that predicts secondary-structure features rivaling experimental chemical probing.

A self-supervised metabolomic foundation model pretrained on NMR metabolite profiles from 430,000+ UK Biobank participants, applied without backbone retraining to aging, subtyping, and risk tasks.

A spatial-transcriptomics foundation model for the tumor microenvironment that produces TME-aware embeddings and enables in silico perturbation from a fixed pretrained checkpoint.

SE(3)-invariant masked autoencoder pretrained on ~370K AlphaFold-DB structures for protein fold representation learning, enabling frozen-feature and zero-shot fold classification.

A mechanistic-interpretability framework that trains sparse autoencoders on protein language model embeddings to extract interpretable features for zero-shot variant effect prediction.

A residual VQ-VAE tokenizer that learns a discrete alphabet of protein conformational ensembles from molecular dynamics data, usable as a frozen representation layer for downstream tasks.

An 85M-parameter conditional discrete diffusion model for protein variant generation with a calibrated identity dial to steer similarity to a wild-type sequence.