All Competitors

Deep graph contrastive learning framework for single-cell proteomics embedding, handling peptide uncertainty, missingness, and batch effects.

Diffusion model for synthesizing single-cell RNA-seq data, with guided generation of specific cell types, rare cells, and developmental trajectories.

Generative RNA design model that samples family sequences from a VAE latent space constrained by Rfam covariance models and consensus structure.

Google's dermatology image embedding model that produces 6144-dimensional embeddings for data-efficient skin-condition classifiers.

Histopathology foundation model that encodes 224x224 H&E patches into compact 384-dimensional embeddings for tumor and biomarker classifiers.

RNA foundation model trained on chemical mapping data, with embeddings adapted to predict RNA secondary and tertiary structure and mRNA stability.

500M-parameter transformer model pretrained on intracranial SEEG recordings for neural signal forecasting, imputation, and seizure detection.

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

EEG pretraining framework mapping any electrode montage to a unified topology for topology-agnostic representations that transfer across datasets.

Self-supervised foundation models for wearable PPG and ECG signals, trained with contrastive learning on Apple Heart and Movement Study recordings.

Health acoustics foundation model that turns short clips of coughs and breaths into embeddings for building acoustic biomarker models with less data.

Vision foundation model for the tree of life, trained on TreeOfLife-10M for zero-shot species classification of plants, animals, and fungi.

Single-cell foundation model producing species-agnostic cell embeddings by representing genes through frozen ESM-2 protein language model embeddings.

Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.

Universal cell segmentation model adapting Meta's SAM to segment mammalian cells, yeast, and bacteria across imaging modalities without retraining.

Generative language model trained on 558 million antibody sequences for infilling-based design of CDR loops and full-length immunoglobulin sequences.

EEG foundation model that pairs a convolutional encoder with a GPT backbone, pretrained by masked-segment reconstruction for low-data BCI decoding.

DNA language model for genome-wide variant effect prediction, trained by masked language modeling on multispecies genomes with no labeled data.

Protein language models from 151M to 6.4B parameters, trained on over a billion sequences for sequence generation and zero-shot fitness prediction.

Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.

Encoder-decoder framework unifying molecules, proteins, and natural language with SELFIES notation for cross-modal drug discovery tasks.

DNA language model for variant effect prediction across coding and non-coding regions, using whole-genome alignments of 100 vertebrate species.

MSA-free protein structure prediction that replaces multiple sequence alignments with a protein language model pre-trained on billions of sequences.

Multi-modal ophthalmic foundation model for generalist eye AI, spanning fundus imaging and OCT for disease screening, segmentation, and biomarkers.