All Competitors

Multimodal physiological foundation model spanning EEG, ECG, EOG, and EMG that keeps working when arbitrary modalities are missing at inference time.

Single-cell foundation model inferring context-specific protein-protein interactions from cancer transcriptomes via a variational graph autoencoder.

Single-cell type annotation pairing a CellMarker-derived knowledge graph with multi-agent LLM retrieval, generalizing across 11 tissue types.

Structure-aware protein language model aligning sequence and 3D structure by contrastive learning, with adapter and LoRA fine-tuning tools.

RNA-seq splice junction detection pairing transcriptome-guided alignment with a fine-tuned DNABERT scorer, reaching a mean F1 of 0.971.

Cryptic protein binding site prediction from sequence, backed by a database of 5,151 cryptic sites mined from 6 million apo-holo PDB alignments.

Multi-agent LLM framework for autonomous discovery in protein science, pairing generator and critic agents over design and simulation tools.

Multi-resolution vision-language foundation model for histopathology, pretrained on 34M TCGA image-text pairs across four magnifications.

Codon-resolution language models trained on 130 million coding sequences from 20,000 species, learning codon rules for translation and mRNA stability.

Spatial transcriptomics prediction from H&E whole-slide images. One generative checkpoint covers 38,984 genes and 17 organs without fine-tuning.

Linear B-cell epitope prediction using phylogeny-aware fine-tuning of ESM embedders, with taxon-specific models for 19 pathogen groups.

Functional ultrasound reconstruction with neural operators, rebuilding power Doppler brain images from 10-15% of the usual compounded frames.

CRISPR/Cas9 off-target prediction that fine-tunes a DNA language model and gates in chromatin signal, reaching 0.550 PR-AUC on GUIDE-seq data.

Single-cell foundation model contrastively fine-tuned on genome-scale Perturb-seq data to separate perturbed from unperturbed transcriptomic states.

Single-cell foundation model built on bidirectional Mamba blocks and pretrained on 30 million cells for linear-time transcriptome embedding.

TCR-peptide binding prediction that fuses ESM-1b receptor embeddings with MolFormer SMILES embeddings through multi-head cross-attention.

Protein binding affinity prediction from sequence alone, returning pKd and per-residue interface labels instead of a yes-or-no interaction call.

Per-residue pKa prediction from sequence alone, a thin MLP head on frozen ESM-2 embeddings reaching 0.48 RMSE across six titratable residue types.

Per-residue pKa prediction from sequence alone, a thin MLP head on frozen ESM Cambrian embeddings that holds up better on engineered buried variants.

Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.

Topology-guided protein backbone generation that turns hand-drawn 3D curves into designable structures by steering a diffusion sampler with a sketch.

Digital cytology foundation model, a ViT-Base pretrained with iBOT self-supervision on 1.4 million patches from breast, cervical, and thyroid slides.

Whole-genome bacterial pathogenicity prediction from ProtT5 embeddings, alignment-free and taxonomy-agnostic, with per-protein attention scores.