All Competitors

RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.

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

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.

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

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.

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

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

Vision-language pretraining for 3D CT volumes, aligning scans with their radiology reports for zero-shot classification, retrieval, and segmentation.

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

AlphaFold fine-tuned via OpenFold on 944 high-resolution MHC-peptide structures, reaching median peptide RMSD of 0.65 Å on held-out complexes.

LoRA and QLoRA fine-tuning of ESM-2 for token-level prediction of protein binding sites and post-translational modification sites from sequence alone.

RNA 3D structure prediction pipeline pairing a transformer (RNAformer) that predicts inter-nucleotide geometries with Rosetta energy minimization.

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

Chinese medical vision-language model pairing a Vision Transformer with an LLM to caption medical images and answer clinical questions in Chinese.

Chest X-ray vision-language model that generates free-text radiology reports, pairing a CXR-specific image encoder with a 7B LLaMA-2 language model.

Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.

Missense variant pathogenicity predictor built on AlphaFold 2 representations, scoring variants across the human proteome at 0.940 AuROC on ClinVar.

Vision-language pre-training framework for universal brain MRI diagnosis, learning from imaging-report pairs to cover more than ten brain diseases.

Discrete diffusion model for protein sequence and MSA generation, enabling controllable de novo design directly in sequence space without structure.

fMRI foundation model pretrained with masked autoencoding on roughly 6,700 hours of recordings for clinical prediction and network discovery.

Radiology foundation model that reads interleaved 2D and 3D scans with text for diagnosis, visual question answering, and report generation.

Multimodal medical vision-language model for few-shot visual question answering, learning new imaging tasks from in-context examples at inference.