All Competitors

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

Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.

Zebrafish single-cell foundation model built on the Geneformer framework, producing frozen gene and cell embeddings for developmental analysis.

Multimodal foundation model for cardiac biosignals, pretrained by masked modeling on ECG, PPG, and clinical text from ~1.7 million individuals.

Family of CNN foundation models pretrained on multimodal radiology images, a domain-specific alternative to ImageNet transfer learning weights.

Apple's foundation model trained on behavioral signals from wearables, modeling 27 HealthKit metrics to improve predictions across 57 health tasks.

Virtual cell transformer that predicts how cells respond to genetic, chemical, or signaling perturbations, generalizing to unseen cellular contexts.

Decoder-only transformer that recasts ancestral recombination graph inference as next-token prediction, estimating coalescence times from variation.

5.6M-parameter multimodal foundation model fusing fMRI time series with diffusion-MRI structural connectivity in a shared ROI embedding space.

CLIP-based vision-language foundation model for eye imaging, enabling zero-shot disease detection and cross-modal retrieval across 11 modalities.

Multimodal viral foundation model over nucleotide and protein sequence, built for virus discovery, function annotation, and antibody design.

Physiological signal foundation model for ECG, EMG, and EEG pairing learnable multi-scale wavelet decomposition with masked transformer pretraining.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.

Open-source framework for building RNA and DNA foundation models, featuring WCED pretraining for transcriptomics and SNP-aware encoding for genomics.

Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.

Brain foundation model unifying EEG and MEG in a single encoder via a shared discrete tokenizer that transfers across sensor layouts and montages.

Single-cell foundation model inferring context-specific protein-protein interactions from cancer transcriptomes via a variational graph autoencoder.

Codon-resolution language models trained on 130 million coding sequences from 20,000 species, learning codon rules for translation and mRNA stability.

Retrieval-augmented protein language model that conditions ESM-2 on homologous sequences via cross-attention for conditional sequence generation.

Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.

265M-parameter genomic foundation model whose BioToken scheme folds variants and region annotations into the tokens for variant effect prediction.

Genomic language models with disentangled attention, pretrained on prokaryotic and eukaryotic genomes for sequence classification and variant effects.

Histopathology foundation model pretrained with BEiT masked image modeling on 11M+ tissue image tiles for cancer diagnosis and survival prediction.