All Competitors

Developmental time inference for single-cell and spatial transcriptomes, regressing absolute embryonic age from frozen Geneformer embeddings.

Single-cell foundation model that learns cell and gene embeddings by latent-space self-distillation rather than reconstructing masked expression.

Single-cell foundation model fusing scRNA-seq with protein structure embeddings and subcellular localization priors through cross-attention.

Image-to-transcriptome diffusion model generating scGPT-space single-cell RNA embeddings from Cell Painting fluorescence microscopy features.

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

Modality-agnostic transformer pretrained by masked abundance reconstruction on 48,837 proteomics profiles reprocessed from 1,397 PRIDE projects.

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.

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

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

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.