All Competitors

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

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

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

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

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

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

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.

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

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.

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.

Generative medical visual question answering model that pairs a vision encoder with a language model, trained on the 227k-pair PMC-VQA dataset.

Single-cell foundation model pre-trained on 22 million transcriptomes, using rank-based gene encoding for clustering and trajectory inference.

Biomedical vision-language model trained contrastively on 1.6M figure-caption pairs mined from PubMed Central open-access articles.

AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.

Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.

Masked DNA language model trained on over 800 vertebrate genomes and conditioned on species identity to learn conserved regulatory sequence features.

Transformer framework for single-cell multi-omics that predicts cross-modality relationships using heterogeneous graphs of cells, genes, and proteins.

Gene expression prediction from histone modifications, combining self-attention with dense convolutions and transfer learning across cell types.

Transformer predicting gene expression from histone modifications, using promoter-enhancer Hi-C interactions to capture distal regulatory effects.

Generative transformer pretrained on PubMed abstracts for biomedical text generation and mining, including relation extraction and question answering.

DNA language model for interpretable prediction of 4mC, 5hmC, and 6mA methylation sites across species, using multi-scale k-mer BERT encoders.