Selected work

Representative Papers

Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families

Oct 02, 2026

This study addresses the extreme scarcity and unclear cellular origins of broadly neutralizing antibodies, as well as the limited cross-viral-family generalizability of existing discovery tools. To overcome these challenges, this work proposes ImmuneAgent, a closed-loop artificial intelligence system that integrates multimodal reasoning with continual meta-learning and incorporates wet-lab feedback mechanisms to efficiently screen candidate antibodies from unlabeled B-cell repertoires. The proposed system achieves a neutralization rate of 55% and a broad-spectrum antibody yield of 11%. Notably, five candidate antibodies confer 100% protection against influenza in murine models and successfully generalize to human metapneumovirus (hMPV) and human papillomavirus (HPV). Collectively, this research establishes a universal intelligent paradigm for cross-viral-family antibody discovery.

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A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases

Nov 18, 2025

Rare diseases suffer from prolonged diagnostic timelines and fragmented clinical evidence; moreover, general-purpose large language models exhibit limited clinical reasoning capabilities due to scarce real-world electronic health records (EHRs), outdated medical knowledge, and hallucination. To address these challenges, we propose a domain-specific clinical reasoning paradigm centered on “narrative-first, knowledge-enhanced” inference. Our approach comprises: (1) constructing a physician-validated rare-disease reasoning dataset and domain-specific corpus; (2) designing a knowledge graph–anchored retrieval mechanism and phased chain-of-thought training to integrate non-phenotypic evidence (e.g., imaging, functional tests); and (3) enhancing robustness under noisy conditions and phenotypic overlap via instruction fine-tuning, knowledge graph fusion, and structured reasoning. Evaluated on multicenter real-world EHRs and public benchmarks, our method achieves state-of-the-art performance—matching the diagnostic accuracy of senior clinicians while significantly shortening diagnostic pathways and enabling transparent, auditable clinical decision-making.

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Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models

Jul 22, 2025

Existing 5′UTR translation efficiency prediction models suffer from fixed-length input constraints and limited interpretability. To address these limitations, we propose UTR-STCNet—a novel, interpretable deep learning architecture designed for variable-length sequence modeling. It integrates saliency-aware token clustering with a lightweight saliency-guided Transformer to enable multi-scale semantic aggregation and capture both local and long-range dependencies. By eliminating the need for sequence truncation, UTR-STCNet balances computational efficiency with biological interpretability. On three benchmark datasets, UTR-STCNet consistently outperforms state-of-the-art methods in predicting ribosomal load. Moreover, it successfully identifies key regulatory motifs—including upstream AUGs and Kozak sequences—demonstrating its capacity for biologically meaningful pattern discovery. This work establishes a new paradigm for functional interpretation and rational design of 5′UTRs.

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Recent publications

Latest Papers

Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families

Oct 02, 2026

This study addresses the extreme scarcity and unclear cellular origins of broadly neutralizing antibodies, as well as the limited cross-viral-family generalizability of existing discovery tools. To overcome these challenges, this work proposes ImmuneAgent, a closed-loop artificial intelligence system that integrates multimodal reasoning with continual meta-learning and incorporates wet-lab feedback mechanisms to efficiently screen candidate antibodies from unlabeled B-cell repertoires. The proposed system achieves a neutralization rate of 55% and a broad-spectrum antibody yield of 11%. Notably, five candidate antibodies confer 100% protection against influenza in murine models and successfully generalize to human metapneumovirus (hMPV) and human papillomavirus (HPV). Collectively, this research establishes a universal intelligent paradigm for cross-viral-family antibody discovery.

0 citationsRead paper

A Specialized Large Language Model for Clinical Reasoning and Diagnosis in Rare Diseases

Nov 18, 2025

Rare diseases suffer from prolonged diagnostic timelines and fragmented clinical evidence; moreover, general-purpose large language models exhibit limited clinical reasoning capabilities due to scarce real-world electronic health records (EHRs), outdated medical knowledge, and hallucination. To address these challenges, we propose a domain-specific clinical reasoning paradigm centered on “narrative-first, knowledge-enhanced” inference. Our approach comprises: (1) constructing a physician-validated rare-disease reasoning dataset and domain-specific corpus; (2) designing a knowledge graph–anchored retrieval mechanism and phased chain-of-thought training to integrate non-phenotypic evidence (e.g., imaging, functional tests); and (3) enhancing robustness under noisy conditions and phenotypic overlap via instruction fine-tuning, knowledge graph fusion, and structured reasoning. Evaluated on multicenter real-world EHRs and public benchmarks, our method achieves state-of-the-art performance—matching the diagnostic accuracy of senior clinicians while significantly shortening diagnostic pathways and enabling transparent, auditable clinical decision-making.

0 citationsRead paper

Decoding Translation-Related Functional Sequences in 5'UTRs Using Interpretable Deep Learning Models

Jul 22, 2025

Existing 5′UTR translation efficiency prediction models suffer from fixed-length input constraints and limited interpretability. To address these limitations, we propose UTR-STCNet—a novel, interpretable deep learning architecture designed for variable-length sequence modeling. It integrates saliency-aware token clustering with a lightweight saliency-guided Transformer to enable multi-scale semantic aggregation and capture both local and long-range dependencies. By eliminating the need for sequence truncation, UTR-STCNet balances computational efficiency with biological interpretability. On three benchmark datasets, UTR-STCNet consistently outperforms state-of-the-art methods in predicting ribosomal load. Moreover, it successfully identifies key regulatory motifs—including upstream AUGs and Kozak sequences—demonstrating its capacity for biologically meaningful pattern discovery. This work establishes a new paradigm for functional interpretation and rational design of 5′UTRs.

0 citationsRead paper