Publications

2025

LKE-DTA: predicting drug–target binding affinity with large language model representations and knowledge graph embeddings

Published in Molecular Diversity, 2025

This work introduces LKE-DTA, a novel deep learning framework that synergistically integrates large language models (LLMs) with knowledge graphs (KGs) to create comprehensive multi-dimensional representations for drugs and proteins for drug-target binding affinity (DTA) prediction.

Recommended citation: Mou, J., Yan, Y., Jiang, B., Yang, F., Pan, Z., Huang, X., Bai, M., Han, Z., & Li, Y. (2025). "LKE-DTA: predicting drug–target binding affinity with large language model representations and knowledge graph embeddings." Molecular Diversity.
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VARIDT 4.0: distribution variability of drug transporters

Published in Nucleic Acids Research (IF=13.1, CAS Ranking: Q2 Top), 2025

This paper introduces VARIDT 4.0, a database focused on the distribution variability of drug transporters, published in Nucleic Acids Research.

Recommended citation: Li, Y., Yang, F., Pan, Z., Yan, Y., Jiang, B., Huang, X., Wang, H., Qin, X., Zeng, S., Fu, T., & Zhu, F. (2025). "VARIDT 4.0: distribution variability of drug transporters." Nucleic Acids Research. Accepted.
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Efficient fine-tuning of small-parameter large language models for biomedical bilingual multi-task applications

Published in Applied Soft Computing, 2025

This study proposes an efficient fine-tuning framework for small-parameter large language models (LLMs) to handle biomedical bilingual multi-task applications, balancing performance with computational efficiency.

Recommended citation: Li, Y., Yan, Y., Tong, Z., Wang, Y., Yang, Y., Bai, M., Pu, D., Xie, J., Liu, C., Li, B., Liu, M., & Shu, K. (2025). "Efficient fine-tuning of small-parameter large language models for biomedical bilingual multi-task applications." Applied Soft Computing, 175, 113084.
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Lung Cancer Biomarker Database (LCBD): A Comprehensive and Curated Repository of Lung Cancer Biomarkers

Published in BMC Cancer, 2025

This paper introduces the Lung Cancer Biomarker Database (LCBD), a centralized, curated platform designed to consolidate fragmented biomarker data to aid in early screening and personalized treatment of lung cancer.

Recommended citation: Li, Y., Tong, Z., Yang, Y. et al. (2025). "Lung Cancer Biomarker Database (LCBD): a comprehensive and curated repository of lung cancer biomarkers." BMC Cancer. 25:478.
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Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models

Published in Journal of Pharmaceutical Analysis, 2025

This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles, knowledge graph embeddings, and large language model representations to enhance drug repurposing predictions.

Recommended citation: Yan, Y., Yang, Y., Tong, Z., Wang, Y., Yang, F., Pan, Z., Liu, C., Bai, M., Xie, Y., Li, Y., Shu, K., & Li, Y. (2025). "Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models." Journal of Pharmaceutical Analysis, 15(6), 101275.
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2024

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Published in Genome Biology, 2024

This paper presents a systematic evaluation of 49 simulation methods for single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data, providing practical guidelines for selecting appropriate simulators.

Recommended citation: Duo, H., Li, Y., Lan, Y. et al. (2024). "Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios." Genome Biology. 25:145.
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DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

Published in Computers in Biology and Medicine, 2024

This paper introduces DTNPD, a specialized, comprehensive database of drugs and targets for neurological and psychiatric disorders (NPDs) to address data quality and coverage gaps in existing resources.

Recommended citation: Luo, D., Tong, Z., Wen, L., Bai, M., Jin, X., Liu, Z., Li, Y., & Xue, W. (2024). "DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders." Computers in Biology and Medicine. 175:108536.
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A Comparative Benchmarking and Evaluation Framework for Heterogeneous Network-Based Drug Repositioning Methods

Published in Briefings in Bioinformatics, 2024

This paper presents a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods, providing a comprehensive framework to evaluate their performance, scalability, and usability.

Recommended citation: Li, Y., Yang, Y., Tong, Z. et al. (2024). "A Comparative Benchmarking and Evaluation Framework for Heterogeneous Network-Based Drug Repositioning Methods." Briefings in Bioinformatics. 25(3):bbae172.
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2022

Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network

Published in Food Research International, 2022

This work develops and compares three structure-taste relationship models based on artificial neural networks to predict whether a molecule is a bitterant, sweetener, or neither.

Recommended citation: Bo, W., Qin, D., Zheng, X., Wang, Y., Ding, B., Li, Y., & Liang, G. (2022). "Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network." Food Research International. 153:110974.
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2020

Clinical trials, progression-speed differentiating features, and swiftness rule of the innovative targets of first-in-class drugs

Published in Briefings in Bioinformatics, 2020

This paper analyzes the clinical trial timelines of 89 innovative targets of first-in-class drugs to identify features that differentiate the speed of clinical progression.

Recommended citation: Li, Y. H., Li, X. X., Hong, J. J., Wang, Y. X., Fu, J. B., Yang, H., Yu, C. Y., Li, F. C., Hu, J., Xue, W. W., Jiang, Y. Y., Chen, Y. Z., & Zhu, F. (2020). "Clinical trials, progression-speed differentiating features, and swiftness rule of the innovative targets of first-in-class drugs." Briefings in Bioinformatics. 21(2):649-662.
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2018

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Published in Nucleic Acids Research, 2018

This paper describes a major update to the Therapeutic Target Database (TTD), enhancing its utility for patient-focused research and clinical investigation of targeted therapeutics.

Recommended citation: Li, Y. H., Yu, C. Y., Li, X. X., Zhang, P., Tang, J., Yang, Q., Fu, T., Zhang, X., Cui, X., Tu, G., Zhang, Y., Li, S., Yang, F., Sun, Q., Qin, C., Zeng, X., Chen, Z., Chen, Y. Z., & Zhu, F. (2018). "Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics." Nucleic Acids Research. 46(D1):D1121-D1127.
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2016

The human kinome targeted by FDA approved multi-target drugs and combination products: a comparative study from the drug-target interaction network perspective

Published in PLoS ONE, 2016

This paper provides a comparative network analysis of FDA-approved multi-target drugs and combination products that target the human kinome, offering insights for next-generation polypharmacology.

Recommended citation: Li, Y. H., Wang, P. P., Li, X. X., Yu, C. Y., Yang, H., Zhou, J., Xue, W. W., Tan, J., & Zhu, F. (2016). "The human kinome targeted by FDA approved multi-target drugs and combination products: a comparative study from the drug-target interaction network perspective." PLoS ONE. 11(11):e0165737.
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SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity

Published in PLoS ONE, 2016

This paper details major improvements to the SVM-Prot web-server, a machine learning tool for predicting protein functional families from sequences, complementing similarity-based methods.

Recommended citation: Li, Y. H., Xu, J. Y., Tao, L., Li, X. F., Li, S., Zeng, X., Chen, S. Y., Zhang, P., Qin, C., Zhang, C., Chen, Z., Zhu, F., & Chen, Y. Z. (2016). "SVM-Prot 2016: a web-server for machine learning prediction of protein functional families from sequence irrespective of similarity." PLoS ONE. 11(8):e0155290.
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Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information

Published in Nucleic Acids Research, 2016

This paper details a major update to the Therapeutic Target Database (TTD), significantly increasing its coverage of clinical trial drugs and targets, and cross-linking them to major pathway databases.

Recommended citation: Yang, H., Qin, C., Li, Y. H., Tao, L., Zhou, J., Yu, C. Y., Xu, F., Chen, Z., Zhu, F., & Chen, Y. Z. (2016). "Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information." Nucleic Acids Research. 44(D1):D1069-D1074.
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