All Competitors

Asymmetric encoder-decoder transformer for single-cell RNA-seq that encodes only non-zero genes, cutting FLOPs 10-100x versus standard transformers.

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

Phenotypic screening model for neuronal activity, encoding network-level calcium dynamics of iPSC-derived neurons into single-cell embeddings.

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

Single-cell foundation model trained by metric learning to embed scRNA-seq profiles for cell type annotation and similarity search in cell atlases.

860M-parameter generative single-cell foundation model that jointly represents and generates epigenomic, transcriptomic, and proteomic modalities.

Single-cell transcriptomic aging clock predicting immune age for CD8+, CD4+ T and NK cells, and transferring to bulk whole-blood RNA-seq.

Single-cell foundation model with rank and expression-aware input streams, pairing masked gene modeling with cell-level contrastive learning.

Transformer foundation model for single-cell ATAC-seq that embeds both cells and cis-regulatory elements for annotation and batch correction.

Single-cell foundation model combining regulatory network priors with a Mamba backbone for clustering, batch integration, and perturbation modeling.

Single-cell cancer model scoring driver-associated expression programs by projecting scRNA-seq through axes frozen from genotype-matched bulk tumors.

Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.

Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.

Single-cell language model that prepends biomedical knowledge-graph tokens to cell sentences, grounding cell type annotation in pathway structure.

Temporal diffusion framework for single-cell developmental dynamics, interpolating and forecasting cell states from irregularly sampled time series.

Single-cell foundation model that compresses each expression profile into 64 cross-attention patch tokens for annotation and spatial transfer.

Framework turning single-cell expression profiles into ranked gene-name sequences, letting off-the-shelf language models generate and annotate cells.

Multimodal LLM that tokenizes single cells into discrete VQ-VAE codebook tokens, letting one model reason jointly over transcriptomes and text.

Multimodal graph foundation model fusing single-cell expression, biomedical text, and signaling networks, pretrained on ~80M sc/snRNA-seq profiles.

Virtual cell foundation model pretrained on over 23 million cells from 5,000 patient samples for drug target and biomarker discovery.

Single-cell perturbation prediction model that forecasts transcriptional responses to drug combinations and doses never experimentally measured.

Single-cell foundation model pre-trained on 50 million cells for gene network inference, denoising, and cell type prediction.

Causal multimodal transformer that embeds the do-operator in attention to predict single-cell gene expression under unseen genetic perturbations.