All Competitors

A VAE trained on scRNA-seq reference data and applied frozen at inference to impute unmeasured genes and denoise spatial transcriptomics profiles.

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

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

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

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

Bayesian deep generative model that integrates gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation of transcription factors.

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 Qwen3-4B language model that reads and reasons over single cells by tokenizing scRNA-seq with residual vector quantization and training with verifiable reinforcement learning.

A transcriptome foundation model for precision oncology that generalizes zero-shot across tissue, plasma cfRNA, and tumor-educated platelet biopsy modalities.

A transcriptomics-native single-cell foundation model that couples batch-invariant representation learning with probabilistic virtual-cell generation.

GRN-informed single-cell foundation model combining gene regulatory hierarchy priors with long-sequence Mamba modeling for clustering, batch integration, perturbation modeling, and drug response prediction.

Billion-parameter single-cell foundation model performing full self-attention across all 28,000 human genes, integrating Gene Ontology priors via GCN for long-range gene context capture in transcriptomics.

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.

Virtual cell foundation model pairing LLaMA-based cellular encoding with set-aware conditional flow matching to predict single-cell perturbation responses at atlas scale.

4.9 billion parameter diffusion language model for predicting genome-wide genetic perturbation responses, trained on the largest CRISPRi Perturb-seq dataset built to date.

Lightweight multimodal foundation model integrating spatial transcriptomics and H&E histopathology with pathway activity scores for biologically grounded spatial niche discovery at single-cell resolution.