All Competitors

Single-cell perturbation-response model predicting transcriptomic and cell-number changes for unseen perturbations plus inverse design.

Contrastive multimodal model for perturbation screens, aligning transcriptomic signatures with text and cell-painting image embeddings.

Vision-guided model that builds virtual 3D organoid surrogates from brightfield microscopy to predict chemical perturbation responses without omics.

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

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

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.

Single-cell foundation model that tokenizes scRNA-seq into 10 tokens in a Qwen3-4B vocabulary for cell type annotation and perturbation prediction.

Billion-parameter single-cell foundation model with self-attention over 28,000 human genes, adding Gene Ontology priors via a graph neural network.

Diffusion language model with 4.9 billion parameters that predicts genome-wide CRISPRi perturbation responses in single-cell transcriptomes.

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

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.

Multimodal single-cell foundation model whose multiway Transformer jointly models scRNA-seq and scATAC-seq from RNA-only, ATAC-only, or paired inputs.

Single-cell perturbation prediction model using conditional flow matching to map control cells to perturbed expression distributions.

Single-cell foundation model built on masked discrete diffusion, jointly generating gene identities and expression values from 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 predict responses to arbitrary perturbations without fine-tuning.

Spatially aware transcriptomic foundation models for cancer, pairing 50um-Local and 250um-Extended views of spot-resolution spatial transcriptomes.

Single-cell foundation model adapting LLaMA-3.1-8B with LoRA, recasting transcriptomes and protein interaction networks as natural-language Q&A pairs.

Spatial transcriptomics language model that reads tissue as spatial sentences to simulate cell profiles and run in silico perturbations.

Latent diffusion model for generating single-cell gene expression profiles, pairing a permutation-invariant autoencoder with a diffusion transformer.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.