Fuyi Li

Research Interests

Bioinformatics and computational biology Knowledge Representation and Machine Learning Machine learning Cancer Biology and Clinical Oncology Artificial Intelligence

Dr Fuyi Li

NHMRC Externally-Funded Research Fellow C

Optimisation and Stabilisation

Integration Management Office

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

Dr Fuyi Li is the Group Leader of Artificial Intelligence for Biological Innovation (ABI Lab) in SAiGENCI. His lab focuses on developing Artificial Intelligence and Machine Learning approaches for cancer research. Dr Li finished his PhD in 2020 at Monash University under the supervision of Professor Jiangning Song and Professor Trevor Lithgow. He then joined the laboratory of Professor Lachlan Coin as a bioinformatics research fellow (Level B) at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne. With a strong background in machine learning and a profound understanding of bioinformatics, Dr Li has garnered recognition for his pioneering work in developing advanced data-driven bioinformatics algorithms and tools. His primary focus lies in tackling intricate biological challenges by harnessing the power of these innovative computational approaches. Dr Li's research interests are at the forefront of the rapidly evolving field of bioinformatics. His work centres on the development and application of cutting-edge machine-learning techniques to interpret vast and diverse biological datasets. These datasets encompass a wide spectrum of biological information, ranging from a wide range of biomolecules data, omics data and histopathology image data. He has developed over 40 bioinformatics software/webservers, and these tools have been used/downloaded in >80 countries, processing >1,200,000 calculation jobs.

I am motivated to investigate, develop, and deploy cutting-edge computational methodologies to better understand and address a range of open and challenging problems in bioinformatics. One of my key contributions is developing AI-driven approaches to study gene expression regulation. This program yielded 35 published bioinformatics approaches (1st-, co-1st, and corresponding author) that enhanced prediction and analysis of gene regulation across genomics, transcriptomics, and proteomics, covering genomic elements annotation to Biomacromolecular Covalent Modification prediction, including DNA modifications, RNA post- transcriptomic modifications, and protein post-translational modifications (PTMs). Among these, 12 papers were Clarivate Highly Cited and 2 were Hot Papers. The research cited and used my tools are from countries including the US, UK, Japan, China, etc., showing the global impacts.

My developed tools have enhanced both academic and industrial pursuits, particularly in refining biomarker identification. For instance:

- Procleave [PMID: 32413515] (1st author, FWCI 4.40, 88 citations, >60,000 job submissions) has impacted many fields including structural biology, microbiology, agriculture, and industry field. (i) Procleave identified cleavage sites for proteins linked to neurodegenerative diseases, such as TMEM106B, enhancing understanding of TMEM106B’s role in amyloid fibril formation and contributing to potential therapeutic targets exploration for these disorders [PMID: 35247328, Cell, research from the US]. (ii) In microbiology, Procleave predicted the Esp743 cleavage site on the Enterococcal surface protein, influencing biofilm formation and antimicrobial resistance studies [PMID: 36103556, PLOS Pathogens, research from the US]. (iii) In agricultural biotechnology, Procleave predicted the cleavage site for Mpf2Ba1 protein activation, crucial for forming pores in insect cells. This enhances Mpf2Ba1’s effectiveness against the western corn rootworm, improving pest control and crop protection [PMID: 37443175, Nat Commun., research from the UK]. (iv) Procleave also impacted neurological research by predicting caspase-3 cleavage sites in Collectin-12, informed studies on brain immunity and neurodegenerative diseases’ phagocytic activity [PMID: 36906641, Cell Death Dis., research from Sweden]. Moreover, (v) a US company was satisfied with Procleave’s capabilities and purchased a 1-year commercial license for two protease-specific models. This transaction underscores Procleave’s commercial viability and potential to impact novel therapeutic development.

- My other tools have also had broad impacts, i.e., DeepCleave [PMID: 31566664, Bioinformatics] (1st author, FWCI 8.47, ESI top 1%, 116 citations, > 48,000 job submissions) impacted proteomics, enhanced understanding of protein degradation linked to O-GlcNAc [PMID: 35705054, Cell Reports], and supported CD95L studies, a protein involved apoptosis signalling, aiding in identifying novel regulatory mechanisms [PMID: 36694998]. GlycoMine [PMID: 25568279, Bioinformatics] (1st author, FWCI 4.61, 189 citations, >76,000 job submissions) was used in the human surfaceome study to predict glycosylation sites, distinguishing surface from intracellular proteins, and aiding targeted drug development [PMID: 30373828, PNAS]. A Swiss company was satisfied with ProsperousPlus [PMID: 37874948] (1st author)'s capabilities and showed their great interest in purchasing the commercial license, currently negotiating.

Date Position Institution name
2023 - ongoing Group leader University of Adelaide
2020 - 2023 Bioinformatics Research Officer University of Melbourne

Language Competency
Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
English Can read, write, speak, understand spoken and peer review

Year Citation
2025 Guo, X., Ran, Z., & Li, F. (2025). Kinase-inhibitor binding affinity prediction with pretrained graph encoder and language model.. Briefings in bioinformatics, 26(4), bbaf338.
DOI
2025 Liu, J., Roy, M. J., Isbel, L., & Li, F. (2025). Accurate PROTAC-targeted degradation prediction with DegradeMaster. Bioinformatics, 41(Supplement_1), i342-i351.
DOI Scopus4 WoS4 Europe PMC2
2025 Hao, Y., Guo, X., Ran, Z., Bi, Y., & Li, F. (2025). LncTracker: a unified multi-channel framework for multi-label lncRNA localization. IEEE Journal of Biomedical and Health Informatics, PP(1), 1-12.
DOI
2025 Zhang, C., Pu, X., Teng, G., Li, F., Bai, H., Lin, K., . . . Tian, F. (2025). pH-Sensitive Metal–Organic Frameworks for the Improved Inhibition of HepG2 Cell via Folate Receptor-Mediated Targeting and Cascaded CDT Effect. Advanced Healthcare Materials, e04093.
DOI
2025 Ran, Z., Guo, X., Pan, T., Bi, Y., Hao, Y., Sun, H., . . . Li, F. (2025). A scalable equivariant graph network framework for precise protein function prediction. Genome Biology, 26(1), 23 pages.
DOI Scopus1
2025 Wu, C., Zhang, N., Li, H., Wang, H., Han, L., Wang, Y., . . . Tian, F. (2025). Preparation of immobilized xanthine oxidase with magnetic metal–organic framework and its application in screening of active ingredients in traditional Chinese medicine. Microchimica Acta, 192(5), 12 pages.
DOI Scopus3 WoS3 Europe PMC2
2025 Zhao, Z., Shi, G., Wu, X., Ren, R., Gao, X., & Li, F. (2025). DIG-Mol: A Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction. IEEE Journal of Biomedical and Health Informatics, 29(3), 1735-1746.
DOI Scopus4 WoS3 Europe PMC3
2024 Lappan, R., Chown, S. L., French, M., Perlaza-Jiménez, L., Macesic, N., Davis, M., . . . Greening, C. (2024). Towards integrated cross-sectoral surveillance of pathogens and antimicrobial resistance: Needs, approaches, and considerations for linking surveillance to action. Environment International, 192, 109046-1-109046-19.
DOI Scopus12 WoS12 Europe PMC5
2024 Zhang, Z., Liu, Y., Xiao, M., Wang, K., Huang, Y., Bian, J., . . . Li, F. (2024). Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis. Briefings in Bioinformatics, 25(6).
DOI
2024 Song, R., J Sutton, G., Li, F., Liu, Q., & Wong, J. J. -L. (2024). Variable calling of m6A and associated features in databases: a guide for end-users. Briefings in Bioinformatics, 25(5), bbae434-1-bbae434-13.
DOI
2024 Liu, T., Jia, C., Bi, Y., Guo, X., Zou, Q., & Li, F. (2024). scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.. Brief Bioinform, 25(6), 12 pages.
DOI Scopus11 WoS8 Europe PMC9
2024 Jia, R., He, Z., Wang, C., Guo, X., & Li, F. (2024). MetalPrognosis: A Biological Language Model-Based Approach for Disease-Associated Mutations in Metal-Binding Site Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(6), 1-10.
DOI Scopus4 WoS3 Europe PMC2
2024 Sweeney, C. J., Bottoms, M., & Schulz, L. (2024). Soil-specific outcomes in the OECD 216 Nitrogen Transformation Test. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT, 25(6), 14 pages.
DOI Scopus6 WoS8 Europe PMC2
2024 Ran, Z., Wang, C., Sun, H., Pan, S., & Li, F. (2024). Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models. IEEE Journal of Biomedical and Health Informatics, 28(9), 1-9.
DOI Scopus4 WoS4 Europe PMC2
2024 Li, F., Bi, Y., Guo, X., Tan, X., Wang, C., & Pan, S. (2024). Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.. Bioinformatics (Oxford, England), 40(8), btae504.
DOI Scopus9 WoS7 Europe PMC5
2024 Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G. I., & Song, J. (2024). Advancing microRNA target site prediction with transformer and base-pairing patterns.. Nucleic acids research, 52(19), gkae782.
DOI Scopus3 WoS4 Europe PMC1
2024 Wang, X., Li, F., Zhang, Y., Imoto, S., Shen, H. H., Li, S., . . . Song, J. (2024). Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects. Briefings in Bioinformatics, 25(5), 15 pages.
DOI Scopus11 WoS9
2024 Wang, X., Patil, N., Li, F., Wang, Z., Zhan, H., Schmidt, D., . . . Song, J. (2024). PmxPred: A data-driven approach for the identification of active polymyxin analogues against gram-negative bacteria. Computers in Biology and Medicine, 168, 107681-1-107681-10.
DOI Scopus17 WoS5 Europe PMC3
2024 Wang, L., Zheng, Y., Jin, D., Li, F., Qiao, Y., & Pan, S. (2024). Contrastive Graph Similarity Networks. ACM Transactions on the Web, 18(2), 17-1-17-20.
DOI Scopus26 WoS22
2024 Wang, C., He, Z., Jia, R., Pan, S., Coin, L. J. M., Song, J., & Li, F. (2024). PLANNER: a multi-scale deep language model for the origins of replication site prediction. IEEE Journal of Biomedical and Health Informatics, 28(4), 2445-2454.
DOI Scopus10 WoS8 Europe PMC9
2024 Yan, Z., Ge, F., Liu, Y., Zhang, Y., Li, F., Song, J., & Yu, D. J. (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.
DOI Scopus18 WoS17 Europe PMC14
2024 He, Z., Wang, C., Guo, X., Sun, H., Bi, Y., Pitt, M. E., . . . Li, F. (2024). MERITS: a web-based integrated <i>mycobacterial</i> PE/PPE protein database. Bioinformatics Advances, 4(1), 8 pages.
DOI Europe PMC1
2023 Chen, J., Wang, M., Zhao, D., Li, F., Wu, H., Liu, Q., & Li, S. (2023). MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data. Interdisciplinary Sciences – Computational Life Sciences, 15(1), 100-110.
DOI Scopus11 WoS11 Europe PMC6
2023 Zhang, Y., Ge, F., Li, F., Yang, X., Song, J., & Yu, D. -J. (2023). Prediction of Multiple Types of RNA Modifications via Biological Language Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(5), 3205-3214.
DOI Scopus23 WoS16 Europe PMC14
2023 Li, F., Wang, C., Guo, X., Akutsu, T., Webb, G. I., Coin, L. J. M., . . . Song, J. (2023). ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.. Briefings in Bioinformatics, 24(6), 14 pages.
DOI Scopus18 WoS20 Europe PMC15
2023 Ge, F., Li, C., Iqbal, S., Muhammad, A., Li, F., Thafar, M. A., . . . Yu, D. J. (2023). VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. Briefings in Bioinformatics, 24(1), 1-16.
DOI Scopus18 WoS18 Europe PMC18
2023 Chen, R., Li, F., Guo, X., Bi, Y., Li, C., Pan, S., . . . Song, J. (2023). ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species. Briefings in Bioinformatics, 24(3), 15 pages.
DOI Scopus18 WoS17 Europe PMC18
2023 Jia, X., Zhao, P., Li, F., Qin, Z., Ren, H., Li, J., . . . Song, J. (2023). ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning. Briefings in Bioinformatics, 24(2), 13 pages.
DOI Scopus9 WoS6 Europe PMC4
2023 Xu, J., Li, F., Li, C., Guo, X., Landersdorfer, C., Shen, H. H., . . . Song, J. (2023). iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Briefings in Bioinformatics, 24(4), 1-20.
DOI Scopus71 WoS55 Europe PMC54
2023 Zhu, Y., Li, F., Guo, X., Wang, X., Coin, L. J. M., Webb, G. I., . . . Jia, C. (2023). TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters.. Briefings in bioinformatics, 24(4), bbad209.
DOI Scopus8 WoS7 Europe PMC6
2023 Bu, Y., Jia, C., Guo, X., Li, F., & Song, J. (2023). COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants.. Briefings in functional genomics, 22(3), 274-280.
DOI Scopus4 WoS4 Europe PMC2
2023 Li, F., Guo, X., Bi, Y., Jia, R., Pitt, M. E., Pan, S., . . . Song, J. (2023). Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.. Computers in biology and medicine, 163, 107155.
DOI Scopus11 WoS10 Europe PMC10
2022 Li, F., Guo, X., Xiang, D., Pitt, M. E., Bainomugisa, A., & Coin, L. J. M. (2022). Computational analysis and prediction of PE_PGRS proteins using machine learning.. Computational and structural biotechnology journal, 20, 662-674.
DOI Scopus27 WoS24 Europe PMC19
2022 Bi, Y., Li, F., Guo, X., Wang, Z., Pan, T., Guo, Y., . . . Song, J. (2022). Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. Briefings in Bioinformatics, 23(6), bbac467-1-bbac467-12.
DOI Scopus23 WoS22 Europe PMC17
2022 Liu, Q., Fang, H., Wang, X., Wang, M., Li, S., Coin, L. J. M., . . . Song, J. (2022). DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions.. Bioinformatics (Oxford, England), 38(17), 4053-4061.
DOI Scopus14 WoS14 Europe PMC12
2022 Iqbal, S., Ge, F., Li, F., Akutsu, T., Zheng, Y., Gasser, R. B., . . . Song, J. (2022). PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations.. Journal of chemical information and modeling, 62(17), 4270-4282.
DOI Scopus35 WoS34 Europe PMC30
2022 Chen, Z., Liu, X., Zhao, P., Li, C., Wang, Y., Li, F., . . . Song, J. (2022). iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Research, 50(W1), W434-W447.
DOI Scopus67 WoS64 Europe PMC59
2022 Lin, Y., Huo, P., Li, F., Chen, X., Jiang, Y., Zhang, Y., . . . Yang, L. (2022). A critical review on cathode modification methods for efficient Electro-Fenton degradation of persistent organic pollutants. CHEMICAL ENGINEERING JOURNAL, 450, 13 pages.
DOI WoS78
2022 Zhu, L., Wang, X., Li, F., & Song, J. (2022). PreAcrs: a machine learning framework for identifying anti-CRISPR proteins. BMC Bioinformatics, 23(1), 444-1-444-21.
DOI Scopus14 WoS11 Europe PMC12
2022 Peng, X., Wang, X., Guo, Y., Ge, Z., Li, F., Gao, X., & Song, J. (2022). RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins. Briefings in Bioinformatics, 23(4), 1-15.
DOI Scopus20 WoS16 Europe PMC12
2022 Wang, M., Li, F., Wu, H., Liu, Q., & Li, S. (2022). PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest. Interdisciplinary Sciences – Computational Life Sciences, 14(3), 697-711.
DOI Scopus7 WoS7 Europe PMC4
2022 Zhang, M., Jia, C., Li, F., Li, C., Zhu, Y., Akutsu, T., . . . Song, J. (2022). Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. Briefings in Bioinformatics, 23(2), 1-25.
DOI Scopus26 WoS24 Europe PMC18
2022 Wang, X., Li, F., Xu, J., Rong, J., Webb, G. I., Ge, Z., . . . Song, J. (2022). ASPIRER: A new computational approach for identifying non-classical secreted proteins based on deep learning. Briefings in Bioinformatics, 23(2), 1-12.
DOI Scopus22 WoS15 Europe PMC19
2022 Packiam, K. A. R., Ooi, C. W., Li, F., Mei, S., Tey, B. T., Ong, H. F., . . . Ramanan, R. N. (2022). PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli. Computational and Structural Biotechnology Journal, 20, 2909-2920.
DOI Scopus16 WoS14 Europe PMC10
2022 Chen, J., Li, F., Wang, M., Li, J., Marquez-Lago, T. T., Leier, A., . . . Song, J. (2022). BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data. Frontiers in Big Data, 4, 16 pages.
DOI Scopus4 WoS1 Europe PMC1
2022 Li, F., Dong, S., Leier, A., Han, M., Guo, X., Xu, J., . . . Song, J. (2022). Positive-unlabeled learning in bioinformatics and computational biology: A brief review. Briefings in Bioinformatics, 23(1), bbab461-1-bbab461-13.
DOI Scopus61 WoS55 Europe PMC44
2021 Iqbal, S., Li, F., Akutsu, T., Ascher, D. B., Webb, G. I., & Song, J. (2021). Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations. Briefings in Bioinformatics, 22(6), 1-23.
DOI Scopus40 WoS39 Europe PMC36
2021 Wang, Y., Coudray, N., Zhao, Y., Li, F., Hu, C., Zhang, Y. -Z., . . . Song, J. (2021). HEAL: an automated deep learning framework for cancer histopathology image analysis.. Bioinformatics (Oxford, England), 37(22), 4291-4295.
DOI Scopus27 WoS22 Europe PMC18
2021 Zhu, Y. -H., Hu, J., Ge, F., Li, F., Song, J., Zhang, Y., & Yu, D. -J. (2021). Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features. Briefings in Bioinformatics, 22(3), 1-14.
DOI Scopus19 WoS15 Europe PMC13
2021 Chai, D., Jia, C., Zheng, J., Zou, Q., & Li, F. (2021). Staem5: A novel computational approachfor accurate prediction of m5C site.. Molecular therapy. Nucleic acids, 26, 1027-1034.
DOI Scopus26 WoS26 Europe PMC21
2021 Wang, Y., Li, F., Bharathwaj, M., Rosas, N. C., Leier, A., Akutsu, T., . . . Song, J. (2021). DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases. Briefings in Bioinformatics, 22(4), 1-12.
DOI Scopus15 WoS20 Europe PMC9
2021 Mei, S., Li, F., Xiang, D., Ayala, R., Faridi, P., Webb, G. I., . . . Song, J. (2021). Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules. Briefings in Bioinformatics, 22(5), 1-16.
DOI Scopus47 WoS47 Europe PMC43
2021 Jia, C., Zhang, M., Fan, C., Li, F., & Song, J. (2021). Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(5), 1937-1945.
DOI Scopus19 WoS19 Europe PMC7
2021 Zhu, Y., Li, F., Xiang, D., Akutsu, T., Song, J., & Jia, C. (2021). Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks. Briefings in Bioinformatics, 22(4), 1-11.
DOI Scopus54 WoS58 Europe PMC37
2021 Xu, J., Li, F., Leier, A., Xiang, D., Shen, H. -H., Marquez Lago, T. T., . . . Song, J. (2021). Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.. Briefings in Bioinformatics, 22(5), 1-22.
DOI Scopus118 WoS107 Europe PMC85
2021 Li, F., Guo, X., Jin, P., Chen, J., Xiang, D., Song, J., & Coin, L. J. M. (2021). Porpoise: a new approach for accurate prediction of RNA pseudouridine sites. Briefings in Bioinformatics, 22(6), 1-12.
DOI Scopus48 WoS47 Europe PMC37
2021 Chen, H., Li, F., Wang, L., Jin, Y., Chi, C. -H., Kurgan, L., . . . Shen, J. (2021). Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions. Briefings in Bioinformatics, 22(3), 1-21.
DOI Scopus39 WoS28 Europe PMC18
2021 Li, F., Chen, J., Ge, Z., Wen, Y., Yue, Y., Hayashida, M., . . . Song, J. (2021). Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework. Briefings in Bioinformatics, 22(2), 2126-2140.
DOI Scopus61 WoS61 Europe PMC51
2021 Ozols, M., Eckersley, A., Platt, C. I., Stewart-Mcguinness, C., Hibbert, S. A., Revote, J., . . . Sherratt, M. J. (2021). Predicting proteolysis in complex proteomes using deep learning. International Journal of Molecular Sciences, 22(6), 1-20.
DOI Scopus18 WoS15 Europe PMC13
2021 Chen, Z., Zhao, P., Li, C., Li, F., Xiang, D., Chen, Y. -Z., . . . Song, J. (2021). iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Research, 49(10), 1-19.
DOI Scopus216 WoS181 Europe PMC161
2021 Liang, X., Li, F., Chen, J., Li, J., Wu, H., Li, S., . . . Liu, Q. (2021). Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Briefings in bioinformatics, 22(4), 1-17.
DOI Scopus68 WoS64 Europe PMC47
2021 Li, M., Wang, Y., Li, F., Zhao, Y., Liu, M., Zhang, S., . . . Xia, J. (2021). A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction. IEEE/ACM transactions on computational biology and bioinformatics, 18(5), 1801-1810.
DOI Scopus56 WoS51 Europe PMC44
2021 Liu, Q., Chen, J., Wang, Y., Li, S., Jia, C., Song, J., & Li, F. (2021). DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites. Briefings in Bioinformatics, 22(3), 1-14.
DOI Scopus107 WoS98 Europe PMC70
2020 Li, F., Fan, C., Marquez-Lago, T. T., Leier, A., Revote, J., Jia, C., . . . Song, J. (2020). PRISMOID: A comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Briefings in Bioinformatics, 21(3), 1069-1079.
DOI Scopus36 WoS35 Europe PMC29
2020 Li, F., Chen, J., Leier, A., Marquez-Lago, T., Liu, Q., Wang, Y., . . . Song, J. (2020). DeepCleave: A deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics, 36(4), 1057-1065.
DOI Scopus108 WoS102 Europe PMC77
2020 Zhu, Y., Jia, C., Li, F., & Song, J. (2020). Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Analytical Biochemistry, 593, 1-10.
DOI Scopus59 WoS43 Europe PMC20
2020 Li, P., Zhang, H., Zhao, X., Jia, C., Li, F., & Song, J. (2020). Pippin: A random forest-based method for identifying presynaptic and postsynaptic neurotoxins. Journal of Bioinformatics and Computational Biology, 18(2), 2050008.
DOI Scopus3 WoS3 Europe PMC3
2020 Li, F., Leier, A., Liu, Q., Wang, Y., Xiang, D., Akutsu, T., . . . Song, J. (2020). Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information. Genomics, Proteomics and Bioinformatics, 18(1), 52-64.
DOI Scopus84 WoS76 Europe PMC81
2020 Chen, Z., Zhao, P., Li, F., Marquez-Lago, T. T., Leier, A., Revote, J., . . . Song, J. (2020). iLearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in Bioinformatics, 21(3), 1047-1057.
DOI Scopus350 WoS323 Europe PMC247
2020 Chen, Z., Zhao, P., Li, F., Wang, Y., Smith, A. I., Webb, G. I., . . . Song, J. (2020). Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences. Briefings in Bioinformatics, 21(5), 1676-1696.
DOI Scopus114 WoS109 Europe PMC88
2020 Bi, Y., Xiang, D., Ge, Z., Li, F., Jia, C., & Song, J. (2020). An Interpretable Prediction Model for Identifying N⁷-Methylguanosine Sites Based on XGBoost and SHAP. Molecular Therapy : Nucleic Acids, 22, 362-372.
DOI Scopus138 WoS129 Europe PMC74
2020 Jia, C., Bi, Y., Chen, J., Leier, A., Li, F., & Song, J. (2020). PASSION: An ensemble neural network approach for identifying the binding sites of RBPs on circRNAs. Bioinformatics, 36(15), 4276-4282.
DOI Scopus79 WoS76 Europe PMC51
2020 Ozols, M., Eckersley, A., Platt, C., McGuinness, C., Hibbert, S., Revote, J., . . . Sherratt, M. (2020). Predicting and validating protein degradation in proteomes using deep learning.
DOI
2020 Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Webb, G. I., . . . Song, J. (2020). PROSPECT: A web server for predicting protein histidine phosphorylation sites. Journal of Bioinformatics and Computational Biology, 18(4), 2050018.
DOI Scopus26 WoS26 Europe PMC20
2019 Li, F., Wang, Y., Li, C., Marquez-Lago, T. T., Leier, A., Rawlings, N. D., . . . Song, J. (2019). Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: A comprehensive revisit and benchmarking of existing methods. Briefings in Bioinformatics, 20(6), 2150-2166.
DOI Scopus77 WoS74 Europe PMC53
2019 Chen, Z., Liu, X., Li, F., Li, C., Marquez-Lago, T., Leier, A., . . . Song, J. (2019). Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Briefings in Bioinformatics, 20(6), 2267-2290.
DOI Scopus102 WoS97 Europe PMC80
2019 Wang, X., Li, C., Li, F., Sharma, V. S., Song, J., & Webb, G. I. (2019). SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models. BMC Bioinformatics, 20(1), 12 pages.
DOI Scopus14 WoS10 Europe PMC7
2019 Zhang, M., Li, F., Marquez-Lago, T. T., Leier, A., Fan, C., Kwoh, C. K., . . . Jia, C. (2019). MULTiPly: A novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics, 35(17), 2957-2965.
DOI Scopus111 WoS106 Europe PMC73
2019 Ma, X., Zhang, L., Song, J., Nguyen, E., Lee, R. S., Rodgers, S. J., . . . Daly, R. J. (2019). Characterization of the Src-regulated kinome identifies SGK1 as a key mediator of Src-induced transformation. Nature Communications, 10(1), 16 pages.
DOI Scopus25 WoS26 Europe PMC27
2019 Mei, S., Li, F., Leier, A., Marquez-Lago, T. T., Giam, K., Croft, N. P., . . . Song, J. (2019). A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Briefings in Bioinformatics, 21(4), 1119-1135.
DOI Scopus134 WoS122 Europe PMC113
2019 Dunstan, R. A., Pickard, D., Dougan, S., Goulding, D., Cormie, C., Hardy, J., . . . Lithgow, T. (2019). The flagellotropic bacteriophage YSD1 targets Salmonella Typhi with a Chi-like protein tail fibre. Molecular Microbiology, 112(6), 1831-1846.
DOI Scopus28 WoS28 Europe PMC28
2019 Li, F., Zhang, Y., Purcell, A. W., Webb, G. I., Chou, K. C., Lithgow, T., . . . Song, J. (2019). Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics, 20(1), 1-17.
DOI Scopus71 WoS66 Europe PMC38
2019 Song, J., Wang, Y., Li, F., Akutsu, T., Rawlings, N. D., Webb, G. I., & Chou, K. C. (2019). iProt-Sub: A comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites. Briefings in Bioinformatics, 20(2), 638-658.
DOI Scopus174 WoS149 Europe PMC107
2018 Li, F., Li, C., Marquez-Lago, T. T., Leier, A., Akutsu, T., Purcell, A. W., . . . Chou, K. C. (2018). Quokka: A comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome. Bioinformatics, 34(24), 4223-4231.
DOI Scopus152 WoS148 Europe PMC109
2018 Chen, Z., Zhao, P., Li, F., Leier, A., Marquez-Lago, T. T., Wang, Y., . . . Song, J. (2018). iFeature: A Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 34(14), 2499-2502.
DOI Scopus580 WoS534 Europe PMC407
2018 Song, J., Li, F., Leier, A., Marquez-Lago, T. T., Akutsu, T., Haffari, G., . . . Pike, R. N. (2018). PROSPERous: High-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy. Bioinformatics, 34(4), 684-687.
DOI Scopus128 WoS125 Europe PMC98
2018 Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K. C., & Webb, G. I. (2018). PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology, 443, 125-137.
DOI Scopus129 WoS120 Europe PMC81
2018 Wei, L., Hu, J., Li, F., Song, J., Su, R., & Zou, Q. (2018). Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Briefings in Bioinformatics, 21(1), 106-119.
DOI Scopus115 WoS109 Europe PMC66
2017 Li, F., Song, J., Li, C., Akutsu, T., & Zhang, Y. (2017). PAnDE: Averaged n-dependence estimators for positive unlabeled learning. Icic Express Letters Part B Applications, 8(9), 1287-1297.
Scopus7
2016 Li, F., Li, C., Revote, J., Zhang, Y., Webb, G. I., Li, J., . . . Lithgow, T. (2016). GlycoMinestruct : A new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features. Scientific Reports, 6(1), 1-16.
DOI Scopus84 WoS78 Europe PMC55
2015 Li, F., Li, C., Wang, M., Webb, G. I., Zhang, Y., Whisstock, J. C., & Song, J. (2015). GlycoMine: A machine learning-based approach for predicting N-, C-and O-linked glycosylation in the human proteome. Bioinformatics, 31(9), 1411-1419.
DOI Scopus180 WoS163 Europe PMC132
2006 Chang, P. C., Chi, C. W., Chau, G. Y., Li, F. Y., Tsai, Y. H., Wu, J. C., & Lee, Y. H. W. (2006). DDX3, a DEAD box RNA helicase, is deregulated in hepatitis virus-associated hepatocellular carcinoma and is involved in cell growth control. ONCOGENE, 25(14), 1991-2003.
DOI WoS136 Europe PMC122
1998 Li, F. Y., & Zhang, Q. Z. (1998). Ordering contraction mapping principle and applications. ACTA MATHEMATICA SCIENTIA, 18(4), 457-460.
DOI

Year Citation
2023 Chen, Z., Li, F., Wang, X., Wang, Y., Kurgan, L., & Song, J. (2023). Designing Effective Predictors of Protein Post-Translational Modifications Using iLearnPlus. In L. Kurgan (Ed.), Machine Learning in Bioinformatics of Protein Sequences: Algorithms, Databases and Resources for Modern Protein Bioinformatics (pp. 309-328). WORLD SCIENTIFIC.
DOI
2023 Guo, X., Li, F., & Song, J. (2023). Predicting Pseudouridine Sites with Porpoise. In Methods in Molecular Biology (Vol. 2624, pp. 139-151). Springer US.
DOI Scopus1
2022 Chen, Z., Liu, X., Li, F., Li, C., Marquez-Lago, T., Leier, A., . . . Song, J. (2022). Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL. In D. B. KC (Ed.), Computational Methods for Predicting Post-Translational
Modification Sites. Methods in Molecular Biology (Vol. 2499, pp. 205-219). Springer US.
DOI Scopus1 Europe PMC1

Year Citation
2025 Pan, J., Liu, Y., Zheng, X., Zheng, Y., Liew, A. W. C., Li, F., & Pan, S. (2025). A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-25) Vol. 39 (pp. 12443-12451). Philadelphia, Pennsylvania: Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus8 WoS3
2024 Zhang, Z., Liu, Y., Bian, J., Yepes, A. J., Shen, J., Li, F., . . . Salim, F. D. (2024). Boosting Patient Representation Learning via Graph Contrastive Learning. In A. Bifet, T. Krilavicius, I. Miliou, & S. Nowaczyk (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14949 LNAI (pp. 335-350). LITHUANIA, Vilnius: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus2 WoS1
2023 Liu, Y., Ding, K., Lu, Q., Li, F., Zhang, L. Y., & Pan, S. (2023). Towards Self-Interpretable Graph-Level Anomaly Detection. In Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS, 2023) ) as published in Advances in Neural Information Processing Systems Vol. 36 (pp. 1-13). Online: Neural information processing systems foundation.
Scopus62
2003 Cockburn, B., Li, F. Y., & Shu, C. W. (2003). Discontinuous Galerkin methods for equations with divergence-free solutions: preliminary results. In K. J. Bathe (Ed.), COMPUTATIONAL FLUID AND SOLID MECHANICS 2003, VOLS 1 AND 2, PROCEEDINGS (pp. 1900-1902). MA, MIT, CAMBRIDGE: ELSEVIER SCIENCE BV.

Year Citation
2025 Lv, B., Huang, X., Zhou, Q., Li, M., Xiao, X., & Li, F. (2025). Diet-Seg: Dynamic Hardness-Aware Learning for Enhanced Brain Tumor Segmentation.
DOI
2025 Liu, J., Roy, M., Isbel, L., & Li, F. (2025). Accurate PROTAC targeted degradation prediction with DegradeMaster.
DOI Europe PMC1
2025 Guo, X., Ran, Z., & Li, F. (2025). Kinase-Inhibitor Binding Affinity Prediction with Pretrained Graph Encoder and Language Model.
DOI
2025 Hao, Y., Guo, X., Ran, Z., Bi, Y., & Li, F. (2025). LncTracker: a unified multi-channel framework for multi-label lncRNA localization.
DOI

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Co-Supervisor To explore the possibility of using Artificial Intelligence models to answer questions about the immune system's fight against cancer Doctor of Philosophy Doctorate Full Time Mr Galen Raphael Pereira
2025 Co-Supervisor To explore the possibility of using Artificial Intelligence models to answer questions about the immune system's fight against cancer Doctor of Philosophy Doctorate Full Time Mr Galen Raphael Pereira

Date Role Editorial Board Name Institution Country
2022 - ongoing Board Member BMC Bioinformatics Springer Nature United States
2020 - ongoing Board Member Frontiers in Bioinformatics Frontiers United States

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