All Competitors

Tissue reconstruction model placing dissociated single cells into spatial coordinates by predicting pairwise distances in a learned embedding space.

Multi-task cellular foundation model predicting drug sensitivity, perturbation expression and drug-protein binding from one pretrained checkpoint.

Single-cell foundation model pretrained on human brain organoids that predicts transcriptome-wide responses to knockdown of any protein-coding gene.

Single-cell perturbation prediction model that splits a transcriptional response into systematic, perturbation-specific, and population-level parts.

Drug- and dose-conditioned latent transition predictor pretrained on the Tahoe-100M perturbation atlas and transferred frozen to tumor RNA-seq.

Genomic foundation model that pairs 650 kb of gene-centered DNA with transcription factor activity to predict expression in unseen cell types.

Gene regulatory network inference from scRNA-seq that returns directed TF-to-target edges in one forward pass, with no per-dataset refitting.

Cross-modal continued pretraining on curated mass-spectrometry proteomes lifts a 70M single-cell model past RNA-only checkpoints far larger.

World model that simulates a human cell as one persistent state, propagating drug and gene perturbations from DNA through to whole-cell morphology.

Single-cell perturbation model that generates a transcriptome gene by gene, letting a regulatory-network policy choose which genes come first.

Single-cell cytometry model that tokenizes each cell as marker-expression pairs, letting studies with different antibody panels share one encoder.

Single-cell perturbation response prediction by conditional latent diffusion, trained on the Tahoe-100M atlas of 100 million drug-treated cells.

Single-cell metabolome inference from scRNA-seq, learned from spatially paired Visium and MALDI-MSI sections by multiple-instance learning.

Virtual-cell model that compresses a transcriptome into eight discrete tokens in a reasoning LLM's vocabulary, predicting module-level drug response.

Context-specific protein embeddings across 286 liver disease and cell-type combinations, learned over interactomes built from a single-cell atlas.

Single-cell foundation model that predicts latent representations of graph-connected gene blocks instead of reconstructing individual gene counts.

Single-cell foundation model for maize, pretrained on a 385,675-cell atlas with Gene Ontology priors for cell typing and cross-species transfer.

Spatial transcriptomics foundation model giving gene-, cell- and neighborhood-scale embeddings zero-shot, plus in-silico gene knockout in tissue.

Single-cell foundation model adding a gated cell-level contrastive objective to masked expression pretraining for transferable frozen cell embeddings.

Cellular foundation models predicting how human cells respond to genetic and pharmacological perturbation, trained on petascale multi-omic data.

Cross-species single-cell ageing-state classifier that transfers mouse age labels to human HSC and CD8+ T cells, reaching 0.953 held-out AUROC.

Spatial proteomics foundation model trained on over 51 million single cells to learn panel-robust cell representations across platforms.

Single-cell perturbation prediction model trained only on synthetic priors, inferring drug targets, intervention strengths, and regulatory graphs.