All Competitors

Regulatory genomics model predicting cell-type-specific RNA-seq coverage from DNA sequence, unifying transcription, splicing, and polyadenylation.

Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.

Multimodal 80B-parameter protein-language model that answers natural language questions about protein function from sequence and structure.

Text-guided protein design framework aligning language with sequences for text-conditioned generation, zero-shot editing, and property prediction.

Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.

Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.

Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.

Knowledge-enhanced ECG foundation model aligning a ResNet encoder with LLM-generated disease descriptions for zero- and few-shot interpretation.

Wearable accelerometry foundation model distilled from a PPG encoder, predicting cardiovascular and health biomarkers from motion signals alone.

Differentiable wave-optical framework for computational microscopy, recovering biomolecular density and orientation across label-free modalities.

Codon optimization framework that adds a generative head to a frozen ProtBert protein language model to design highly expressed coding sequences.

Biomedical multimodal LLM that answers questions about medical images and returns pixel-level segmentation masks, using a mixture-of-experts design.

Multimodal foundation model for wearable physiological sensing across PPG, ECG, EEG, GSR, and IMU signals, using channel-aware attention.

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

EEG foundation model for brain-computer interface decoding, factorizing self-attention into parallel spatial and temporal branches.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Generative model that emulates protein equilibrium ensembles, sampling cryptic pockets and unfolded states far faster than molecular dynamics.

Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.

Latent diffusion model for controllable all-atom protein generation that co-designs sequence and structure while training on sequences alone.

Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.

DNA foundation model scaling an encoder-only transformer to 7 billion parameters for variant effect prediction, gene expression, and sequence design.

Protein language model family at 300M, 600M, and 6B parameters, purpose-built for representation learning and outperforming ESM-2 at smaller scale.

Mixture-of-experts protein language model scaling to 16 billion parameters, applied to variant effect prediction and de novo protein design.