All Competitors

Foundation model spanning invasive SEEG/iEEG and non-invasive EEG in one backbone, with zero- and few-shot transfer across neurological disorders.

Conversational T5-based framework that turns scRNA-seq data into cell sentences for cell type annotation and drug sensitivity prediction.

DNA embedding model built on DNABERT-2, using contrastive learning to cluster sequences by species for metagenomic binning without labeled data.

RNA secondary structure prediction from a single sequence, without MSAs, using an axial-attention transformer trained with strict homology controls.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

Disentangled variational autoencoder separating batch effects from condition effects in scRNA-seq for key gene detection and perturbation prediction.

Generative language model for single-cell transcriptomics with 368M parameters, unifying cell type annotation, batch integration, and cell generation.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

Promptable foundation model for universal medical image segmentation, fine-tuned from SAM on 1.57M image-mask pairs across 10 imaging modalities.

Multimodal protein pre-training framework jointly learning sequence, 3D structure, and surface representations via implicit neural representations.

RNA language model trained on multiple sequence alignments of Rfam families, predicting secondary structure and solvent accessibility from homology.

Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.

EEG foundation model pretrained with vector-quantized self-supervision, yielding interpretable discrete codes that transfer to seizure detection.

Self-supervised pathology foundation model with a 300M-parameter vision transformer tile encoder, trained on a multi-stain whole-slide image corpus.

Generative image perturbation autoencoder predicting cellular morphological responses to chemical and genetic perturbations from control images.

Protein function prediction models that assign Gene Ontology terms using language model embeddings and neuro-symbolic reasoning over GO axioms.

Codon-level BERT model that captures genomic signals invisible to amino acid models, outperforming billion-parameter PLMs with just 86M parameters.

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.

Cell type annotation for single-cell RNA-seq that builds a graph per signaling pathway, learning across pathway views with graph neural networks.