All Competitors

Nuclei detection and instance segmentation in H&E slide images, where each transformer query carries an anchor circle instead of a bounding box.

Medical imaging adaptation of the Segment Anything Model, fine-tuned on 4.6M images and 19.7M masks for promptable segmentation across 10 modalities.

Vision-language foundation model for pathology, fine-tuned from CLIP on 208,414 image-text pairs for zero-shot classification and image retrieval.

Perturbation prediction model that forecasts transcriptional responses to multi-gene CRISPR perturbations from scRNA-seq and a gene-gene graph.

Antibody CDR design framework pairing a pretrained antibody language model with a hierarchical graph neural network for one-shot CDR generation.

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

Open large language models for natural science, fine-tuned on physics, chemistry, and materials science literature with automated instruction tuning.

Transfer learning framework that predicts single-cell gene expression from ~200kb DNA sequences using Enformer embeddings and a lightweight MLP.

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

Google's generalist multimodal biomedical AI that encodes clinical text, medical images, and genomics with a single set of weights across 14 tasks.

Multi-language transformer framework using five pre-trained language models to predict DNA methylation (6mA, 4mC, 5hmC) across species.

Bilingual protein language model that translates bidirectionally between amino acid sequences and the 3Di structural alphabet for inverse folding.

Protein language model ranking stabilizing mutations with no assay data, trained jointly to predict the growth temperature of each sequence's host.

Transformer-based single particle tracker for fluorescence microscopy, using multi-hypothesis attention to link particles at low SNR and high density.

Self-supervised histopathology foundation models for H&E whole-slide images. Phikon-v2 is a ViT-L/16 DINOv2 encoder used for biomarker prediction.

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.

Gut microbiome language model that reads a 16S sample as a sentence of taxa, producing context-sensitive embeddings that transfer across cohorts.

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

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

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.