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portfolio
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publications
T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model
Published in Briefings in Bioinformatics, 2022
Recommended citation: Y Zhang, Y Zhang, Y Xiong, H Wang, Z Deng, J Song, HY Ou. (2022). T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model. Briefings in Bioinformatics, 23(1), bbab420.
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Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction
Published in Bioinformatics, 2023
Recommended citation: H Gong, Y Zhang, C Dong, Y Wang, G Chen, B Liang, H Li, L Liu, J Xu, et al. (2023). Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction. Bioinformatics, 39(10), btad589.
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DeepSecE: A deep-learning-based framework for multiclass prediction of secreted proteins in gram-negative bacteria
Published in Research, 2023
Recommended citation: Y Zhang, J Guan, C Li, Z Wang, Z Deng, RB Gasser, J Song, HY Ou. (2023). DeepSecE: A deep-learning-based framework for multiclass prediction of secreted proteins in gram-negative bacteria. Research, 6, 0258.
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Enhanced thermostability of Streptomyces mobaraensis transglutaminase via computation-aided site-directed mutations and structural analysis
Published in New Journal of Chemistry, 2024
Recommended citation: Y Li, B Wu, Y Zhang, L Liu, L Bai, T Shi. (2024). Enhanced thermostability of Streptomyces mobaraensis transglutaminase via computation-aided site-directed mutations and structural analysis. New Journal of Chemistry, 48(2), 591-602.
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TransEFVP: a two-stage approach for the prediction of human pathogenic variants based on protein sequence embedding fusion
Published in Journal of Chemical Information and Modeling, 2024
Recommended citation: Z Yan, F Ge, Y Liu, Y Zhang, F Li, J Song, DJ Yu. (2024). TransEFVP: a two-stage approach for the prediction of human pathogenic variants based on protein sequence embedding fusion. Journal of Chemical Information and Modeling, 64(4), 1407-1418.
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Transposition with Tn3-family elements occurs through interaction with the host beta-sliding clamp processivity factor
Published in Nucleic Acids Research, 2024
Recommended citation: Y Tang, J Zhang, J Guan, W Liang, MT Petassi, Y Zhang, X Jiang, et al. (2024). Transposition with Tn3-family elements occurs through interaction with the host beta-sliding clamp processivity factor. Nucleic Acids Research, 52(17), 10416-10430.
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Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor-antigen recognition
Published in Nature Machine Intelligence, 2024
Recommended citation: Y Zhang, Z Wang, Y Jiang, DR Littler, M Gerstein, AW Purcell, J Rossjohn, HY Ou, J Song. (2024). Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor-antigen recognition. Nature Machine Intelligence, 6(11), 1344-1358.
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Histo-genomic knowledge association for cancer prognosis from histopathology whole slide images
Published in IEEE Transactions on Medical Imaging, 2025
Recommended citation: Z Wang, Y Zhang, Y Xu, S Imoto, H Chen, J Song. (2025). Histo-genomic knowledge association for cancer prognosis from histopathology whole slide images. IEEE Transactions on Medical Imaging, 44(5), 2170-2181.
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Supervised contrastive learning enhances MHC-II peptide binding affinity prediction
Published in Expert Systems with Applications, 2025
Recommended citation: LC Shen, Y Liu, Z Liu, Y Zhang, Z Wang, Y Guo, J Rossjohn, J Song, et al. (2025). Supervised contrastive learning enhances MHC-II peptide binding affinity prediction. Expert Systems with Applications, 269, 126463.
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Structure-directed pan-specific t-cell receptor-peptide-major histocompatibility complex interaction prediction
Published in Journal of Chemical Information and Modeling, 2025
Recommended citation: L Gao, Y Zhang, F Ge, S Li, Y Guo, J Song, DJ Yu. (2025). Structure-directed pan-specific t-cell receptor-peptide-major histocompatibility complex interaction prediction. Journal of Chemical Information and Modeling, 65(9), 4674-4686.
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Toward fair AI-driven medical text generation
Published in Nature Computational Science, 2025
Recommended citation: Y Zhang, J Song. (2025). Toward fair AI-driven medical text generation. Nature Computational Science, 5(5), 361-362.
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Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features
Published in Briefings in Bioinformatics, 2025
Recommended citation: Z Sun, J Xu, Y Zhang, Y Zhang, Z Wang, X Wang, et al. (2025). Multimodal geometric learning for antimicrobial peptide identification by leveraging alphafold2-predicted structures and surface features. Briefings in Bioinformatics, 26(3), bbaf261.
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Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes
Published in Nature Machine Intelligence, 2025
Recommended citation: LC Shen, Y Zhang, Z Wang, DR Littler, Y Liu, J Tang, J Rossjohn, DJ Yu, et al. (2025). Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes. Nature Machine Intelligence, 7(8), 1250-1265.
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Deep learning speeds the search for new antibiotic scaffolds
Published in Nature Biotechnology, 2025
Recommended citation: Y Zhang, J Song, C de la Fuente-Nunez. (2025). Deep learning speeds the search for new antibiotic scaffolds. Nature Biotechnology, 1-3.
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UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction
Published in Genome Biology, 2026
Recommended citation: Y Chen, Y Zhang, M Niu, J Wang, Z Ren, Q Zou, J Song, X Luo. (2026). UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction. Genome Biology. 27, 74.
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A geometric deep learning framework for genome-wide prediction of enzyme turnover number
Published in Genome Biology, 2026
Recommended citation: T Pan, X Cui, HY Koh, Y Bi, X Wang, Y Zhang, S Hu, GI Webb, L Kurgan, et al. (2026). A geometric deep learning framework for genome-wide prediction of enzyme turnover number. Genome Biology 27, 92.
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De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning
Published in Advanced Science, 2026
Recommended citation: X Li, H Gong, Y Wang, Y Zhao, L Li, P Bao, Q Kong, J Fu, B Wan, Y Zhang, et al. (2026). De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning. Advanced Science, e15835.
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talks
Genome-scale prediction of bacterial secreted effectors using pre-trained protein language model
Published:
Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition
Published:
Design CDR3αβ sequences tailored to specific peptide-MHC targets by a TCR diffusion model
Published:
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.