All Competitors

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

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.

A deep generative decoder that infers microRNA expression directly from bulk or single-cell mRNA gene expression via a shared mRNA/miRNA latent space.

Multimodal tumor foundation model trained on paired H&E histology and spatial transcriptomics to infer whole-transcriptome and tumor-microenvironment signal from routine H&E alone.

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 zebrafish DNA sequence-to-function model predicting cell-type-specific single-cell expression across 85 cell-type x developmental-timepoint combinations during embryogenesis.

A constrained deep flow-matching framework for distributional translation of omics signatures across biological domains, such as mouse-to-human transcriptomics, without paired samples.

A cross-modal transfer-learning model that adapts the ESM-2 650M protein language model to mRNA analysis by swapping amino-acid tokens for codon tokens, applied to mRNA benchmarks without re-training.

A virtual cell foundation model pretrained on 23M+ cells from 5,000 patient samples for drug target and biomarker discovery.

A causal multimodal Transformer that embeds the do-operator within attention to predict single-cell responses to gene perturbations, including unseen ones.

A multi-modal Transformer that fuses LLM gene embeddings with biological knowledge graphs to predict single-cell transcriptomic responses to genetic perturbations.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few seed perturbations, using one-eighth the parameters of typical single-cell foundation models.

A direction-aware foundation model trained on ~23M bulk RNA-seq differential-expression profiles that simulates coordinated gene dynamics in viral infection.

A foundation model that predicts spatial transcriptomics and proteomics directly from routine H&E whole-slide images using a vision transformer aligned with a language model.

A 1.25B-parameter Mixture-of-Experts genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window.

A supervised, chromatin-informed foundation model that predicts regulatory activity directly from plant genomic sequence in Arabidopsis and rice.

A long-context plant DNA language model (676M params, Mamba2) pretrained on 65 angiosperm genomes for cross-species functional annotation.

Generative discrete-diffusion model that designs regulatory DNA with tunable activity and learns activity-predictive representations rivaling genomic language models.

Generates single-cell transcriptomic profiles from structured biological metadata via contrastive language-omics pretraining and a diffusion transformer.

A generative cellular world model that uses masked discrete diffusion to learn whole-transcriptome scRNA-seq distributions and simulate perturbation responses across tissues and species.

Multipurpose generative adversarial network trained once on single-cell and bulk RNA-seq to perform stratification, marker analysis, data generation, and vectorization.

A BART-based foundation model for mRNA coding-sequence analysis, pretrained by denoising across nine taxonomic groups and fine-tunable for expression, stability, and riboswitch tasks.

Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cellular trajectories over time.

A temporal diffusion-forcing generative framework for simulating, interpolating, and forecasting single-cell developmental dynamics from irregularly sampled time-series data.