All Competitors

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

Bidirectional state-space (Mamba-2) genomic model for ultra-long extrachromosomal circular DNA, scaling linearly with sequence length.

Conditional codon language model with 150M parameters that generates species-optimized coding sequences from a protein and its taxonomic lineage.

All-atom generative model for de novo protein and peptide binder design against diverse biomolecular targets, wet-lab validated across 26 targets.

Generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.

Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.

Retrieval-augmented protein encoders that fuse homologous sequences into a single-pass transformer for variant effect and contact prediction.

Self-supervised Siamese network for cryo-electron tomography, enabling zero-shot denoising, segmentation, and macromolecule detection in tomograms.

Multi-target drug discovery framework pairing a diffusion-transformer generator with evolutionary latent-space search and synthesis-aware scoring.

Single-cell latent diffusion model fine-tuned on 14.5 million CD4+ T cells to simulate transcriptomic effects of single-gene perturbations.

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

Antibody language model pretrained only on CDR-H3 loops, giving embeddings for immune repertoire analysis and antibody sequence classification.

Histopathology foundation model extracting general-purpose features from H&E patches by distilling the UNI, Phikon, and CONCH pathology encoders.

EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.

Perturbation-trained single-cell foundation models (up to 3B parameters) that jointly model genes, cells, and compounds for precision oncology tasks.

fMRI foundation model trained on cortical flat-map videos with masked autoencoding, showing power-law scaling on brain activity reconstruction.

Single-cell foundation model learning technology-agnostic cell embeddings by contrasting cell views rather than reconstructing gene expression counts.

Protein structure tokenizer that maps 3D backbones to discrete tokens with an SE(3)-equivariant encoder preserving orientation and chirality.

Predicts single-cell scRNA-seq coverage and scATAC-seq insertion profiles from DNA sequence, adapting the Borzoi trunk with a cell-specific decoder.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Histology vision transformer with 80M parameters that predicts spatial gene expression from H&E tissue images and transfers to tumor detection.

RNA foundation model pretrained jointly on sequences and secondary structures for structure prediction, homology and splice site classification.

Proteome-scale protein language model whose representations enable zero-shot protein-protein interaction and gene essentiality prediction.