All Competitors

Radiology CT framework pairing lesion-driven contrastive slice embeddings with attention pooling to predict clinical endpoints without fine-tuning.

Histopathology image synthesis steered by a hand-drawn coarse tissue mask, refined into a fine-grained semantic mask before H&E or IHC rendering.

RNA-binding protein binding profiles predicted base by base across 800 bp windows, with an m6A signal channel that exposes methylation-RBP crosstalk.

Spatial transcriptomics foundation model aligning histology with gene expression at spot and neighborhood scale for zero-shot tissue domain calling.

Ultrasound foundation model pretrained by federated learning across 16 institutions, transferring to disease diagnosis and lesion segmentation.

Multi-scale latent diffusion model for histopathology that synthesizes tissue patches at any magnification and composes them into 4096-pixel images.

Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.

Virtual staining diffusion model that converts kidney histology between H&E, Masson's trichrome, PAS, and PASM in any direction from one checkpoint.

Vector-based virtual screening model that co-embeds proteins and small molecules so a drug-target interaction reduces to a single dot product.

Prostate histopathology classifiers that split H&E tissue into benign, Gleason 3, 4 and 5 patches and aggregate those calls into an ISUP grade group.

Conditional denoising diffusion model that synthesizes chest radiographs from patient demographics and echocardiographic ventricular measurements.

Enhancer-promoter interaction prediction from DNA sequence and ATAC-seq alone. Spearman above 0.90 on cell types unseen during training.

Lung cancer histopathology model that predicts spread through air spaces from whole-slide images using a feature-interactive Siamese graph encoder.

Histopathology foundation model aligned to spatial transcriptomics by a cross-modal ranking loss, embedding H&E patches without gene input.

Metabolite annotation model embedding MS/MS spectra and molecular graphs in one contrastive space, ranking candidate structures by cosine similarity.

De novo peptide design across non-canonical amino acid space, using guided diffusion over receptor-ligand interfaces to reach D-amino acid chemistry.

Context-aware protein sequence search, where a genomic language model fine-tuned for retrieval lets neighboring genes shape the query embedding.

Peptide identification for diaPASEF proteomics, scoring fragment coelution across retention time and ion mobility with a pretrained CNN.

Protein-protein interaction prediction from structure alone, embedding each protein once so a proteome-wide dot product replaces pairwise queries.

Single-cell foundation model trained by metric learning to embed scRNA-seq profiles for cell type annotation and similarity search in cell atlases.

Slide-level histopathology encoder that contrastively aligns tile embeddings from several patch foundation models through a Mamba-2 aggregator.

Target-conditioned generative language model that designs drug-like SMILES from a protein sequence, tuned on regulator-approved drug-target pairs.

Hydration-site prediction placing water molecules on protein surfaces by score-based diffusion, within 0.3 Å of crystallographic positions.