Fudan University

Reinforcement-learning generative framework for multi-objective RNA codon optimization that generalizes across six species and five RNA types.

Protein-text foundation model placing amino acid sequences and natural language in one token space for protein understanding and de novo design.

Dirichlet flow-matching model for protein design that generates family-aware sequences from ancestral-reconstruction priors, not random noise.

Codon-level mRNA language model adapted from ESM-2 650M by swapping amino-acid tokens for codon tokens, transferring protein knowledge to mRNA tasks.

Pathology foundation model that infers spatial transcriptomics and proteomics directly from routine H&E whole-slide images, with no spatial assay.

Connects a frozen protein language model to a general LLM via a cross-modal projector, adding protein reasoning without catastrophic forgetting.

De novo peptide sequencing transformer that reads modified and unmodified peptides directly from tandem mass spectra without a reference database.

Medical multimodal LLM (2B and 8B) trained for generalizable, step-by-step clinical reasoning via Mentor-Intern Collaborative Search.

General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.

Ultrasound foundation model pretrained on over two million multi-organ images, transferring to segmentation, classification, and image enhancement.

Swin transformer foundation model for fluorescence microscopy image restoration, unifying denoising, super-resolution, and volumetric reconstruction.

Protein language model-based sequence search that detects remote homologs with threefold higher sensitivity than MMseqs2 at comparable speed.

RNA foundation model pretrained on 23.7 million non-coding RNA sequences, producing embeddings for structure prediction, annotation, and RNA design.