All Competitors

Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.

Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.

Protein domain annotation model pairing an ESM-2 backbone with a probabilistic decoder, bringing language-model sensitivity to Pfam-style assignment.

Wearable sensor foundation model pretrained on heart rate, accelerometer, skin temperature and other channels for activity recognition and imputation.

Self-supervised contrastive model embedding cell and organelle dynamics from time-lapse microscopy for cell-state analysis without manual labels.

Masked-autoencoder foundation model pretrained on digital-stethoscope heart sounds and single-lead ECG for cardiovascular disease detection.

Protein language models trained on biophysical dynamics from MD simulations and normal-mode analysis; ESMDance builds on ESM2 for variant effects.

ECG foundation model pretrained on 12-lead waveforms paired with clinical reports, enabling label-efficient and zero-shot cardiac diagnosis.

Weakly supervised histopathology foundation model pretrained on 60,530 whole-slide images for cancer detection, prognosis, and molecular prediction.

Chest X-ray foundation model that pairs masked image modeling with image-report contrastive alignment for zero-shot diagnosis and phrase grounding.

EEG-to-language foundation model that pairs a Q-Conformer encoder with a frozen LLM to decode coherent sentences from non-invasive brain recordings.

Multi-task EEG foundation model that treats brain signals as a foreign language, pairing a text-aligned neural tokenizer with a GPT-2 backbone.

Mixed-modality genomic language model encoding protein coding sequences as amino acids and intergenic DNA as nucleotides in native genomic context.

Multimodal ECG-language model aligning 12-lead waveforms with clinical report text for conversational cardiac diagnosis and report generation.

Open transformer foundation model for 12-lead electrocardiograms, pretrained on 1.5 million unlabeled ECGs with a wav2vec 2.0 self-supervised recipe.

Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.

Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.

Chest X-ray embedding model built on ELIXR, producing image and image-text embeddings for data-efficient and zero-shot radiograph classification.

SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.

De novo peptide binder design framework that targets specific motifs, including disordered regions and conserved epitopes, from target sequence alone.

Single-cell foundation model pre-trained on 50 million cells for gene network inference, denoising, and cell type prediction.

Chromatin-state language model pretrained on ROADMAP annotations from 127 human cell types to find chromatin-state motifs and predict gene expression.

Multimodal vision-language copilot for pathology that answers open-ended questions about histology images and reasons about differential diagnoses.

Lightweight AlphaFlow variant that fine-tunes only AlphaFold's structure module, keeping the Evoformer frozen to cut conformational sampling cost.