All Competitors

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 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 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.

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.

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.

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

Functional diffusion model that predicts single-cell perturbation responses by generating over distributions embedded in a Hilbert space, capturing population-level response variability.

A discrete-diffusion generative model that operates directly on single-cell gene counts, enabling unconditional and perturbation-conditioned scRNA-seq generation.

A multimodal single-cell foundation model with a multiway Transformer that jointly models scRNA-seq and scATAC-seq, including RNA-only, ATAC-only, and paired inputs.

Distributional flow matching model for single-cell perturbation prediction that models population-level expression shifts using a graph-aware differential-attention transformer.

A masked discrete-diffusion single-cell foundation model that jointly generates cell identity and expression, pre-trained on 59 million cells.

Large causal cell model trained on cancer perturbation data that generalizes zero-shot to patient-derived cells for therapeutic target prioritization.

Single-cell foundation model using tabular attention over context cells to enable zero-shot representation and in-context prediction of arbitrary perturbations.

A pair of spatially aware transcriptomic foundation models (50um-Local and 250um-Extended) for multi-scale analysis of spot-resolution cancer spatial transcriptomes.

A fine-tuned scLDM variant trained on 14.5 million CD4+ T cells for counterfactual prediction of single-gene perturbation effects in immune cells.

A scalable latent diffusion model for generating realistic single-cell gene expression profiles, using a permutation-invariant VAE and flow-matching diffusion transformer.

A graph-signal-processing foundation model that embeds gene regulatory network structure directly into its attention mechanism for parameter-efficient single-cell transcriptomics.

Transformer model for predicting cellular responses to perturbations across diverse cell contexts, trained on over 267 million human single-cell profiles.

A single-cell perturbation model that augments scGPT with gene-level language embeddings from NCBI, UniProt, and Gene Ontology to improve multi-gene perturbation prediction.

Generative image perturbation autoencoder that predicts cellular morphological responses to chemical and genetic perturbations using untreated cell images as input.

Geometric deep learning model that predicts transcriptional responses to multi-gene perturbations by integrating single-cell RNA-seq with a gene-gene knowledge graph.

Compositional Perturbation Autoencoder that predicts single-cell transcriptional responses to unseen drug combinations and doses using disentangled latent representations.