Dr Fuyi Li
Group Leader, Computational Systems Oncology
South Australian Immunogenomics Cancer Institute
Faculty of Health and Medical Sciences
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.
-
Appointments
Date Position Institution name 2023 - ongoing Group leader University of Adelaide 2020 - 2023 Bioinformatics Research Officer University of Melbourne -
Language Competencies
Language Competency Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review -
Research Interests
-
Journals
Year Citation 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.
Scopus22025 Hu, X., Zhu, Y., Shi, L., Zhou, K., Fang, Y., & Li, F. (2025). Synthesis and properties of sodium stearyl polyoxypropylene acetates. JOURNAL OF SURFACTANTS AND DETERGENTS, 28(2), 251-262.
2024 Xu, L., Zhu, J., Rong, L., Yang, H., Wang, B., Lu, S., . . . Zhang, L. (2024). Osteoblast-specific down-regulation of NLRP3 inflammasome by aptamer-functionalized liposome nanoparticles improves bone quality in postmenopausal osteoporosis rats. THERANOSTICS, 14(10), 3945-3962.
2024 Ma, X., Yuan, P., Li, F., & Ji, J. (2024). THREE-DIMENSIONAL MODEL OF EQUIVALENT ROLL GAP OF CVC MILL UNDER CONDITIONS OF ROLL CROSSING AND NO LOAD. MATERIALI IN TEHNOLOGIJE, 58(2), 193-202.
2024 Liu, S., Yang, H., Shu, J., Wu, L., Li, Y., Zhang, Z., . . . Hu, W. (2024). Asymmetric Carbene-Alkyne Metathesis-Mediated Cascade: Synthesis of Benzoxazine Polychiral Polyheterocycles and Discovery of a Novel Pain Blocker. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 63(21), 7 pages.
2024 Wang, Y., Shu, J., Yang, H., Hong, K., Yang, X., Guo, W., . . . Zhang, J. (2024). Nav1.7 Modulator Bearing a 3-Hydroxyindole Backbone Holds the Potential to Reverse Neuropathic Pain. ACS CHEMICAL NEUROSCIENCE, 15(6), 1063-1073.
2024 Chen, B., Li, F., Lin, Y., Yang, L., Wei, W., Ni, B. -J., & Chen, X. (2024). Degradation of Chloroquine by Ammonia-Oxidizing Bacteria: Performance, Mechanisms, and Associated Impact on N<sub>2</sub>O Production. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 58(10), 4662-4669.
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.
Scopus12024 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).
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.
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.
Scopus1 Europe PMC22024 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), 2340-2348.
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), 5649-5657.
Scopus12024 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.
Scopus2 Europe PMC12024 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.
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.
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), 15 pages.
Scopus32024 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.
Scopus10 Europe PMC22024 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.
Scopus72024 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.
Scopus7 Europe PMC52024 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.
Scopus7 Europe PMC32024 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.
2024 Morris, J. N., Loyer, J., & Blunt, J. (2024). Stigma, risks, and benefits of medicinal cannabis use among Australians with cancer. Supportive Care in Cancer, 32(4), 7 pages.
2024 Patel, C., Nicmanis, M., Chur-Hansen, A., & Crawford, G. B. (2024). Views of admitted palliative care patients and their clinicians on corneal donation discussions: a qualitative content analysis of semi-structured interviews. BMC Palliative Care, 23(1), 85-1-85-9.
Scopus22023 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.
Scopus12 Europe PMC62023 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.
Scopus7 Europe PMC32023 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.
Scopus5 WoS3 Europe PMC42023 Long, L., Li, F., & Zhu, X. (2023). Normalized solutions to nonlinear scalar field equations with doubly nonlocal terms and critical exponent. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS, 524(1), 25 pages.
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 (vol 24, bbac535, 2022). BRIEFINGS IN BIOINFORMATICS, 24(1), 1 page.
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.
Scopus15 WoS4 Europe PMC92023 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.
Scopus15 WoS1 Europe PMC102023 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.
Scopus6 Europe PMC22023 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.
Scopus43 WoS2 Europe PMC252023 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), 12 pages.
Scopus4 Europe PMC12023 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.
Scopus4 WoS1 Europe PMC22023 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.
Scopus10 Europe PMC62022 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.
Scopus21 WoS11 Europe PMC112022 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.
Scopus19 WoS4 Europe PMC102022 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.
Scopus10 WoS2 Europe PMC42022 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.
Scopus31 WoS9 Europe PMC222022 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.
Scopus45 WoS12 Europe PMC282022 Yang, H., Shan, Z., Guo, W., Wang, Y., Cai, S., Li, F., . . . Cai, S. (2022). Reversal of Peripheral Neuropathic Pain by the Small-Molecule Natural Product Narirutin via Block of Na<sub>v</sub>1.7 Voltage-Gated Sodium Channel. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 23(23), 18 pages.
WoS1 Europe PMC72022 Chen, X., Li, F., Huo, P., Liu, J., Yang, L., Li, X., . . . Ni, B. -J. (2022). Influences of longitudinal gradients on methane-driven membrane biofilm reactor for complete nitrogen removal: A model-based investigation. WATER RESEARCH, 220, 9 pages.
WoS72022 Chen, X., Liu, J., Huo, P., Li, F., Yang, L., Wei, W., & Ni, B. -J. (2022). Influences of granule properties on the performance of autotrophic nitrogen removal granular reactor: A model-based evaluation. BIORESOURCE TECHNOLOGY, 356, 9 pages.
WoS62022 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.
Scopus9 WoS4 Europe PMC62022 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.
Scopus17 WoS2 Europe PMC72022 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.
Scopus5 WoS1 Europe PMC32022 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.
Scopus22 WoS8 Europe PMC142022 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.
Scopus21 WoS3 Europe PMC112022 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.
Scopus12 WoS3 Europe PMC62022 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.
Scopus22022 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.
Scopus46 WoS16 Europe PMC292021 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.
Scopus34 WoS17 Europe PMC242021 Chen, X., Huo, P., Liu, J., Li, F., Yang, L., Li, X., . . . Ni, B. -J. (2021). Model predicted N<sub>2</sub>O production from membrane-aerated biofilm reactor is greatly affected by biofilm property settings. CHEMOSPHERE, 281, 7 pages.
WoS11 Europe PMC32021 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, 37(22), 4291-4295.
Scopus24 WoS14 Europe PMC132021 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.
Scopus19 WoS9 Europe PMC92021 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.
Scopus24 WoS14 Europe PMC152021 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.
Scopus11 WoS7 Europe PMC72021 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.
Scopus41 WoS23 Europe PMC252021 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.
Scopus18 WoS16 Europe PMC42021 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.
Scopus49 WoS38 Europe PMC302021 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.
Scopus93 WoS32 Europe PMC562021 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.
Scopus46 WoS31 Europe PMC282021 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.
Scopus37 WoS20 Europe PMC102021 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.
Scopus59 WoS46 Europe PMC372021 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.
Scopus16 WoS7 Europe PMC72021 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.
Scopus180 WoS72 Europe PMC1162021 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.
Scopus58 WoS32 Europe PMC262021 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.
Scopus44 WoS23 Europe PMC232021 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.
Scopus100 WoS66 Europe PMC462021 Barclay, K. (2021). Academic Emotions.
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.
Scopus34 WoS27 Europe PMC222020 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.
Scopus104 WoS81 Europe PMC532020 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.
Scopus50 WoS27 Europe PMC112020 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.
Scopus1 WoS12020 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.
Scopus75 WoS47 Europe PMC562020 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.
Scopus328 WoS228 Europe PMC1712020 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.
Scopus106 WoS70 Europe PMC712020 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.
Scopus125 WoS74 Europe PMC452020 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.
Scopus74 WoS42 Europe PMC302020 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.
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.
Scopus24 WoS13 Europe PMC142019 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.
Scopus69 WoS57 Europe PMC382019 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.
Scopus93 WoS68 Europe PMC532019 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.
Scopus13 WoS9 Europe PMC52019 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.
Scopus105 WoS85 Europe PMC562019 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.
Scopus24 WoS21 Europe PMC242019 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.
Scopus115 WoS76 Europe PMC822019 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.
Scopus25 WoS17 Europe PMC202019 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.
Scopus68 WoS58 Europe PMC292019 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.
Scopus167 WoS142 Europe PMC822018 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.
Scopus144 WoS117 Europe PMC752018 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.
Scopus527 WoS351 Europe PMC2662018 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.
Scopus122 WoS106 Europe PMC702018 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.
Scopus122 WoS104 Europe PMC492018 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.
Scopus109 WoS87 Europe PMC482017 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.
Scopus72016 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.
Scopus82 WoS65 Europe PMC442015 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.
Scopus171 WoS133 Europe PMC95 -
Book Chapters
Year Citation 2024 Vasconcelos, R. P., Reis-Santos, P., Henriques, S., Tanner, S. E., Cabral, H. N., Costa, J. L., & Costa, M. J. (2024). River-Coast Connectivity, Estuarine Nursery Function and Coastal Fisheries. In D. Baird, & M. Elliott (Eds.), Treatise on Estuarine and Coastal Science (Second Edition) (pp. 163-205). Elsevier.
DOI2023 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.
DOI2023 Guo, X., Li, F., & Song, J. (2023). Predicting Pseudouridine Sites with Porpoise. In Methods in Molecular Biology (Vol. 2624, pp. 139-151). Springer US.
DOI2022 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 PMC12020 Barclay, K. (2020). Independence, Affection and Mobility in Eighteenth-Century Scotland. In Keeping Family in an Age of Long Distance Trade, Imperial Expansion, and Exile, 1550-1850 (pp. 127-146). Amsterdam University Press.
DOI -
Conference Papers
Year Citation 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 Scopus12023 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.
Scopus242023 Subarkah, D., Nixon, A., Collins, A., Gilbert, S., Blades, M., Virgo, G., . . . Farkas, J. (2023). New ways to date old rocks: novel applications of in situ geochronology to constrain the sedimentary archive. In Goldschmidt2023 abstracts. European Association of Geochemistry.
DOI -
Preprint
Year Citation 2025 Liu, J., Roy, M., Isbel, L., & Li, F. (2025). Accurate PROTAC targeted degradation prediction with DegradeMaster.
DOI2025 Guo, X., Ran, Z., & Li, F. (2025). Kinase-Inhibitor Binding Affinity Prediction with Pretrained Graph Encoder and Language Model.
DOI2025 Hao, Y., Guo, X., Ran, Z., Bi, Y., & Li, F. (2025). LncTracker: a unified multi-channel framework for multi-label lncRNA localization.
DOI2024 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.
DOI2024 Bi, Y., Li, F., Wang, C., Pan, T., Davidovich, C., Webb, G., & Song, J. (2024). Advancing microRNA Target Site Prediction with Transformer and Base-Pairing Patterns.
DOI2023 Jia, R., He, Z., Wang, C., Guo, X., & Li, F. (2023). MetalPrognosis: a Biological Language Model-based Approach for Disease-Associated Mutations in Metal-Binding Site prediction.
DOI Europe PMC12023 Liu, Y., Ding, K., Lu, Q., Li, F., Zhang, L. Y., & Pan, S. (2023). Towards Self-Interpretable Graph-Level Anomaly Detection. 2023 He, Z., Wang, C., Guo, X., Sun, H., Bi, Y., Pitt, M., . . . Li, F. (2023). MERITS: a web-based integrated<i>Mycobacterial</i>PE/PPE protein database.
DOI2023 Li, F., Bi, Y., Guo, X., Tan, X., Wang, C., & Pan, S. (2023). Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.
DOI
-
Current Higher Degree by Research Supervision (University of Adelaide)
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 Principal Supervisor Integrating multi-omics data to identify novel biomarkers for improved disease diagnosis and prognosis Doctor of Philosophy Doctorate Full Time Miss Anxuan Han
-
Editorial Boards
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
Connect With Me
External Profiles
