All Competitors

Histopathology-to-molecular alignment model that queries H&E slides with gene-set signatures to predict pathway activity without sequencing.

Supervised variational autoencoder that learns a tissue-aware latent space for bulk RNA-seq, trained on harmonized TCGA, GTEx, and ARCHS4 data.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.

Bulk RNA-seq foundation model learning normalization-robust transcriptome representations via TF-IDF gene ordering and masked gene modeling.

Genomic foundation model with 120M parameters that learns adaptive DNA token boundaries by dynamic chunking, not fixed k-mer or byte-pair tokens.

Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.

Tumor foundation model that infers whole-transcriptome and microenvironment signal from H&E slides, pretrained on paired spatial transcriptomics.

Transcriptomics foundation model from Recursion that masks and reconstructs RNA-seq gene expression counts to learn reusable sample embeddings.

Zebrafish sequence-to-function model predicting cell-type-specific gene expression from DNA sequence across embryonic development.

Flow-matching framework that translates omics signatures across biological domains, such as mouse to human transcriptomics, without paired samples.

Codon-level mRNA language model adapted from ESM-2 650M by swapping amino-acid tokens for codon tokens, transferring protein knowledge to mRNA tasks.

Virtual cell foundation model pretrained on over 23 million cells from 5,000 patient samples for drug target and biomarker discovery.

Causal multimodal transformer that embeds the do-operator in attention to predict single-cell gene expression under unseen genetic perturbations.

Multi-modal transformer fusing LLM gene embeddings with biological knowledge graphs to predict single-cell responses to genetic perturbations.

Meta-learning framework that transfers perturbation responses across cell lines, donors, and drugs from a few measured seed perturbations.

Direction-aware foundation model trained on bulk RNA-seq differential-expression profiles to simulate coordinated gene dynamics in viral infection.

Pathology foundation model that infers spatial transcriptomics and proteomics directly from routine H&E whole-slide images, with no spatial assay.

Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.

Chromatin-informed foundation model predicting regulatory activity and chromatin state directly from plant genomic sequence in Arabidopsis and rice.

Long-context plant DNA language model, 676M parameters on a Mamba2 backbone, pretrained on 65 angiosperm genomes for cross-species variant annotation.

Discrete diffusion model that designs regulatory DNA with tunable cell-type-specific activity and learns activity-predictive representations.

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

Virtual cell model using masked discrete diffusion over the whole transcriptome to simulate scRNA-seq perturbation responses across tissues.