Sun Yat-sen University

A hyperbolic protein language model for alignment-free phylogenetic inference, producing distance matrices for tree placement without multiple sequence alignment.

Generative virtual-cell engine that unifies clinical RNA-seq and perturbation-assay data to predict transcriptomic responses to unseen compounds and genetic perturbations across biological scales.

A DeepSeek-7B-based multi-task large reasoning model that applies chain-of-thought reasoning and reinforcement learning across ~10 molecular science task families.

A generalizable computational-pathology foundation model trained on ~190M histopathology patches via unified knowledge distillation from UNI, Phikon, and CONCH.

A multi-modal viral foundation model trained on 25.4B nucleotide and amino-acid tokens spanning nearly all known viruses, for virus discovery, function annotation, and antibody design.

A self-improving text-to-image diffusion foundation model that generates synthetic medical images across multiple modalities and organs to augment downstream clinical AI tasks.

Region-aware bilingual (Chinese-English) medical multimodal LLM that handles image- and region-level vision-language tasks across eight imaging modalities.

An 800M-parameter single-cell foundation model pre-trained on 100 million human cells via a RetNet architecture for cell annotation, perturbation prediction, and gene analysis.

A multimodal generalist foundation model for ophthalmic imaging, self-supervised on 2.78M images across 11 modalities for diagnosis, prognosis, and visual question answering.

Prompt-based medical vision-language pretraining that unifies fusion-encoder and dual-encoder architectures, handling image-only, text-only, and image-text inputs in one model.

Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.

Knowledge-enhanced medical vision-and-language pre-training framework that aligns, reasons over, and learns from structured medical knowledge for radiology image-text tasks.