All Competitors

Multimodal model that designs small molecules from transcriptomic and cell-imaging perturbation phenotypes with a rectified flow transformer.

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.

Bidirectional generative framework linking nuclear morphology and gene expression, built on a morphology model trained on 21 million segmented nuclei.

Spatial transcriptomics foundation model built on a lightweight graph convolutional network and trained by masked central-spot prediction.

Single-cell RNA-seq language model that treats cells as gene-expression tokens, synthesizing whole transcriptomes from tissue and disease metadata.

Cross-modal single-cell foundation model that aligns gene-expression profiles with LLM-enriched cell descriptions in a shared embedding space.

Single-cell foundation model using tabular attention over context cells to predict responses to arbitrary perturbations without fine-tuning.

Generative language model for phenotype-driven drug discovery, proposing small-molecule structures from up- and down-regulated gene signatures.

Transcriptomic foundation model pretrained on 67M single-cell and spatial profiles, modeling gene expression and inter-cellular dependencies.

Mixture-of-Experts generative model turning DNA sequence plus cell-type ATAC-seq into unified epigenomic, transcriptomic, and 3D chromatin profiles.

Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.

Multimodal foundation model that embeds histology, transcriptomics, and clinical records in one space for patient stratification and target discovery.

In-context learning model for cell-free RNA, meta-trained on synthetic tasks from a cfRNA structural causal model for few-shot cancer classification.

Pan-cancer single-cell foundation model with a hybrid Transformer-Mamba architecture, released with the PanFoMaBench cancer evaluation benchmark.

Pan-cancer multi-omic foundation model encoding CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.

Conversational single-cell and spatial multi-omics brain foundation model, with zero-shot cell annotation and disease prediction across species.

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.

Hierarchical single-cell foundation model that turns scRNA-seq profiles into zero-shot donor-level embeddings for disease and biomarker prediction.

Generative framework that reconstructs missing spatial transcriptomics regions by jointly predicting cell locations, cell types, and gene expression.

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

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.