All Competitors

Unsupervised transformer language model for TCR-epitope binding prediction that generalizes to unseen epitopes without needing negative examples.

Chromosome-wise explainable autoencoder that compresses DNA methylation array data up to 400-fold while keeping CpG groupings interpretable.

Transformer model predicting context-specific epigenomic signals across cell types using DNA sequence and transcription factor activity profiles.

Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.

Explainable autoencoder for transcriptome analysis that uses SHAP attribution on latent variables to identify critical genes driving gene expression.

RNA foundation model trained on 1 billion sequences, with a 400M-parameter variant for secondary and tertiary structure and functional annotation.

GPT-style DNA foundation model trained on over 200 billion base pairs of mammalian genomes for sequence generation, classification, and regression.

De novo protein design diffusion model that generates backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.

Generative transformer that translates single-cell transcriptomes into proteomes, inferring missing protein abundance from RNA expression alone.

Tokenized graph transformer that embeds longitudinal brain functional connectomes from fMRI for interpretable neurodegenerative disease diagnosis.

Endoscopy video foundation model that learns spatial-temporal representations from unlabeled clips for classification, segmentation, and detection.

Self-supervised foundation model for 3D medical image segmentation, pretrained on roughly 110,000 unannotated CT volumes via Volume Fusion.

Genomic foundation model built on the Hyena operator, processing DNA at single-nucleotide resolution with context windows up to 1 million tokens.

Multi-species genomic foundation model swapping k-mer tokenization for byte pair encoding, matching Nucleotide Transformer with 21x fewer parameters.

3D CT localization foundation model that pairs MedLAM with SAM to segment any anatomical structure at a fixed, dataset-independent annotation cost.

Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.

Vision Transformer for cell instance segmentation and classification in H&E whole-slide images, extended by CellViT++ with foundation backbones.

Family of transformer-based DNA language models using BPE tokenization and BigBird sparse attention to reach context lengths up to 36,000 base pairs.

Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.

Predicts 12 types of RNA modification sites from sequence, fine-tuning DNABERT representations fused with CNN-encoded sequence features.

Biomedical vision-language assistant for question answering on radiology and pathology images, adapted from LLaVA on PubMed Central captions.

Single-cell foundation model pretrained on about 30 million human transcriptomes, using rank-value encoding for context-aware gene network inference.

Multimodal pathology assistant that answers questions about histology and cytology images, pairing the PathCLIP vision encoder with a Vicuna-13B LLM.

Vision-language framework for 3D medical image diagnosis and visual question answering, bridging frozen image encoders and LLMs, shown on brain MRI.