All Competitors

Retrieval-augmented genomic foundation model that gives transformer backbones a hash-based k-mer motif memory for functional genomics tasks.

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.

Partially latent flow-matching model for de novo protein design, jointly generating sequence and all-atom structure for proteins up to 800 residues.

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

Contrastive geometric model unifying structure- and ligand-based drug design for zero-shot virtual screening, target fishing, and pocket selection.

Generative transformer foundation model for continuous glucose monitoring, forecasting glycemia and stratifying health risk from raw glucose traces.

Metagenomic foundation model trained on 9.7 trillion nucleotide tokens for generative therapeutic design across genes, peptides, and microbiomes.

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.

Gut microbiome foundation model pretrained on human shotgun metagenomes, learning species-level taxonomic representations for disease prediction.

Transformer foundation model pretrained on a biomedical knowledge graph for zero-shot drug repurposing, target, and adverse-effect prediction.

3D vision-language foundation model for abdominal CT, pretrained on scans, radiology reports, and EHR codes for zero-shot interpretation.

Native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes, segmenting subcellular structures without voxel labels.

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

470M-parameter microbial genome foundation model trained on 234.5B base pairs for multi-scale genomic representation and trait prediction.

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

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

Microscopy image restoration foundation model unifying 8 tasks across 5 modalities and 2D/3D data, with zero-shot inference on unseen systems.

GPT-style generative language model for mRNA coding sequences, pretrained across bacteria, eukaryotes, and archaea for de novo CDS design.

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

Multi-species genomics foundation model spanning representation learning, functional-track prediction, and sequence generation at 1 Mb context.

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