All Competitors

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

Enhancer RNA mapping model that locates eRNA loci genome-wide from DNA sequence and aggregated RNA-seq signal using a CNN-transformer architecture.

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

Generative framework that learns a developmental vector field from scRNA-seq snapshots, coupling flow matching with molecular RNA kinetics.

Multi-view single-cell foundation model at 12B parameters, aligning expression and perception views with an LLM-JEPA joint-embedding objective.

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.

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

Cell world model pretrained on a 2.4M-cell mouse embryonic atlas, predicting one-step transcriptional state transitions and perturbation response.

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

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

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.

Autoregressive nucleotide-and-text foundation model generating DNA and RNA sequences from natural-language prompts that name species and function.

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

Transcriptome foundation model for precision oncology, generalizing zero-shot across tissue, plasma cfRNA, and tumor-educated platelet modalities.

Transcriptomics-native single-cell foundation model that learns batch-invariant cell representations and probabilistically generates virtual cells.

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

Generative adversarial network trained on single-cell and bulk RNA-seq for sample stratification, marker analysis, and synthetic data generation.

Temporal diffusion framework for single-cell developmental dynamics, interpolating and forecasting cell states from irregularly sampled time series.

Knowledge-graph foundation model for drug repurposing, grounding a biomedical graph in cell-type-specific genetic associations to rank indications.

Diffusion model predicting single-cell responses to genetic or drug perturbations, generating over distributions to capture population variability.

Single-cell foundation model applying discrete diffusion directly to scRNA-seq counts, generating unconditional and perturbation-conditioned profiles.

Single-cell foundation model that forecasts how cancer cells evolve, autoregressively generating future gene expression from prior cell states.