All Competitors

Multimodal foundation model pretrained on 1.76 billion paired histology-spatial transcriptomics spots, linking whole-slide images to spatial molecular programs.

A deep generative decoder that infers microRNA expression directly from bulk or single-cell mRNA gene expression via a shared mRNA/miRNA latent space.

Multimodal tumor foundation model trained on paired H&E histology and spatial transcriptomics to infer whole-transcriptome and tumor-microenvironment signal from routine H&E alone.

Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.

A protein-text foundation model embedding sequences and natural language in a shared token space, enabling protein understanding and de novo design from one checkpoint.

A tri-modal foundation model unifying histology images, spatial transcriptomics, and biological language for zero-shot spatial biology and pathology reasoning.

A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.

Multimodal deep-learning framework that detects and localizes DNA lesions directly from native nanopore sequencing, built on the damage-aware LesionBERT foundation model.

A multimodal Q-former that fuses DNA sequence, gene context, protein function, and text into a prefix for a frozen LLM, enabling zero-shot genetic variant interpretation.

A ligand-conditioned masked discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

A transferable spatial-transcriptomics deconvolution model whose rank-based spot encoding lets one pretrained model generalize across tissues, disease states, and platforms without retraining.

A unified bio-language Mixture-of-Experts foundation model spanning DNA, protein sequence and structure, and biological text, applied across eight task families from a single checkpoint.

A multi-organ foundation model aligning histology image features with spatial-transcriptomics gene expression across 13 organs for zero-shot virtual ST and survival prediction.

A fully atomic protein co-design model using unified multimodal diffusion to jointly refine atom types and coordinates in a single stage, with support for non-canonical amino acids.

A causal multimodal Transformer that embeds the do-operator within attention to predict single-cell responses to gene perturbations, including unseen ones.

A protein language model that aligns ESM sequence embeddings with molecular-dynamics trajectory embeddings via contrastive learning for zero-shot mutation-effect prediction.

A reasoning-guided foundation model for de novo antibody CDR design, pairing a multimodal-LLM understanding expert with a Boltz-1-based diffusion generation expert.

A multi-modal Transformer that fuses LLM gene embeddings with biological knowledge graphs to predict single-cell transcriptomic responses to genetic perturbations.

Generative multimodal foundation model that jointly models DNA, RNA, protein, and cellular context across six biological modalities, with SOTA splicing prediction.

An autoregressive nucleotide-and-text foundation model pretrained on ~416B characters from 2,221 eukaryotic species for natural-language-guided conditional generation of DNA and RNA sequences.

A foundation model that predicts spatial transcriptomics and proteomics directly from routine H&E whole-slide images using a vision transformer aligned with a language model.

A 110M-parameter multimodal RNA language model that designs RNA sequences from secondary structure, consensus, and Gene Ontology constraints via discrete diffusion.

Leakage-controlled multimodal model predicting within-species relative protein expression across 385 bacterial species, with transfer to unseen phyla.

A Qwen3-4B language model that reads and reasons over single cells by tokenizing scRNA-seq with residual vector quantization and training with verifiable reinforcement learning.