All Competitors

Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.

Transformer language model for 5' UTR sequences that predicts mRNA translation efficiency, ribosome loading, and protein expression levels.

Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.

Computational pathology foundation model (ViT-L/16, DINOv2) pretrained on over 100 million H&E tiles from more than 100,000 whole-slide images.

Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.

RNA language model that builds base-pairing constraints into self-attention, pretrained on 20.4 million sequences for structure and function tasks.

Bidirectional, reverse-complement equivariant DNA language models built on Mamba state space models for long-range variant effect prediction.

Protein large language model adapted from LLaMA-2 that unifies sequence generation and superfamily classification in one 7B-parameter framework.

DNA embedding model built on DNABERT-2, using contrastive learning to cluster sequences by species for metagenomic binning without labeled data.

Conversational T5-based framework that turns scRNA-seq data into cell sentences for cell type annotation and drug sensitivity prediction.

Protein conformational ensemble generator that fine-tunes AlphaFold 2 with flow matching, sampling protein dynamics beyond a single static structure.

Promptable foundation model for universal medical image segmentation, fine-tuned from SAM on 1.57M image-mask pairs across 10 imaging modalities.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

RNA language model trained on multiple sequence alignments of Rfam families, predicting secondary structure and solvent accessibility from homology.

Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.

Histopathology foundation model that encodes 224x224 H&E patches into compact 384-dimensional embeddings for tumor and biomarker classifiers.

Google's dermatology image embedding model that produces 6144-dimensional embeddings for data-efficient skin-condition classifiers.

500M-parameter transformer model pretrained on intracranial SEEG recordings for neural signal forecasting, imputation, and seizure detection.

Health acoustics foundation model that turns short clips of coughs and breaths into embeddings for building acoustic biomarker models with less data.

Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.

4-bit QLoRA fine-tunes of ESM-2 for per-residue protein binding site prediction, released as a checkpoint family spanning 8M to 650M parameters.

Full-precision LoRA fine-tuning of ESM-2 for per-residue binding site prediction, where low-rank constraints curb overfitting on small datasets.

EEG foundation model that pairs a convolutional encoder with a GPT backbone, pretrained by masked-segment reconstruction for low-data BCI decoding.