All Competitors

Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.

Multi-view single-cell foundation model at 12B parameters, aligning expression and perception views with an LLM-JEPA joint-embedding objective.

Protein binder generator producing receptor-conditioned binders from sequence alone, using a sparse Mixture-of-Experts transformer with no 3D input.

Mixture-of-Experts genomic foundation model for the human microbiome, with 4.7B parameters pretrained on bacterial, archaeal, and phage genomes.

Unified bio-language Mixture-of-Experts model spanning DNA, protein sequence and structure, and biological text across eight task families.

Genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window and a 1.25B-parameter mixture-of-experts transformer.

Molecular foundation models pretrained on density functional theory data, encoding 3D geometry and quantum behavior for ADMET and drug discovery.

Generative virtual-cell model predicting whole-transcriptome responses to unseen compounds and genetic perturbations, from cell lines to organoids.

Pan-cancer model predicting spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

Retrieval-augmented genomic foundation model that gives transformer backbones a hash-based k-mer motif memory for functional genomics tasks.

Mixture-of-Experts generative model turning DNA sequence plus cell-type ATAC-seq into unified epigenomic, transcriptomic, and 3D chromatin profiles.

Pan-viral genomic language model producing fixed genome-level embeddings of viral DNA and RNA, reused across classification tasks without retraining.

Plant genome foundation model pairing a bidirectional Mamba backbone with sparse Mixture-of-Experts, pretrained on 25.4B nucleotides from 42 species.

EEG foundation model for brain-computer interfaces, pairing masked pretraining with a mixture-of-experts transformer across electrode montages.

Kidney-specialized single-cell foundation model trained across four mammalian species for zero-shot cell-type annotation and batch integration.

Brain MRI foundation model pairing DenseNet and Vision Transformer backbones with mixture of experts for disease diagnosis and brain age prediction.

Protein language models trained on billions of natural and synthetic sequences for de novo design and zero-shot mutation-effect prediction.

Sparse mixture-of-experts autoregressive protein language model family pretrained on 1.5 trillion amino acid tokens with compute-optimal scaling.

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

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

Open foundation model for photoplethysmography (PPG), learning morphology-aware waveform representations for cardiovascular and wearable health tasks.

Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.

Mixture-of-Experts foundation model for medical image segmentation that generalizes across imaging modalities and clinical centers.

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