Thuc Le

Research Interests

Artificial Intelligence Bioinformatics and computational biology Data mining and knowledge discovery Machine learning Statistical data science

APrf Thuc Le

Associate Professor

School of Computer Science and Information Technology

College of Engineering and Information Technology

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

Available For Media Comment.

I am an Associate Professor in Computer Science. I have a diverse educational background with BSc and MSc in Mathematics, BSc in Computer Science, and PhD in Computer Science. I have been awarded the Ian Davey Thesis Prize for the most outstanding PhD thesis in 2014, then received an NHMRC ECR Fellow in Bioinformatics/Computational Biology (2017-2019), and DECRA Fellow (2020-2022). I was a visiting researcher at the University of Michigan in 2015, and a visiting professor at the University of Pennsylvania in 2019.
 
Please feel free to contact me for collaborations.
 

My research focuses on the development of Causal AI methods and their applications across various domains, particularly in Bioinformatics. Bioinformatics is an interdisciplinary field that integrates knowledge from Computer Science, Mathematics, and Statistics to address biological problems. We develop Causal AI methods to uncover gene regulatory networks, identify cancer drivers, investigate the roles of non-coding RNAs in cancer, classify cancer subtypes, and discover potential drug targets.

I am listed as Australia’s top researcher in Bioinformatics and Computational Biology (Engineering and Computer Science) in 2025 and in 2026 by The Australian Research Magazine

Apply for PhD scholarships  here

My publications: Google Scholar and go here to download the papers.

Date Position Institution name
2025 - ongoing Associate Professor Adelaide University
2021 - 2025 Associate Professor University of South Australia
2017 - 2020 Senior Lecturer & DECRA Fellow University of South Australia
2014 - 2017 NHMRC Fellow University of South Australia

Date Type Title Institution Name Country Amount
2025 Recognition listed as Australia’s top researcher in Bioinformatics and Computational Biology (Engineering and Computer Science) in 2026 by The Australian Research Magazine University of South Australia Australia -
2024 Recognition listed as Australia’s top researcher in Bioinformatics and Computational Biology (Engineering and Computer Science) in 2025 by The Australian Research Magazine University of South Australia Australia -
2022 Award Best Paper Award University of South Australia Australia -
2020 Award Best Paper Award University of South Australia Australia -
2015 Award Ian Davey Thesis Prize University of South Australia Australia -

Language Competency
English Can read, write, speak, understand spoken and peer review
Vietnamese Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
2011 - 2014 University of South Australia Australia PhD
2008 - 2010 University of South Australia Australia Bachelor
2003 - 2006 Vietnam National University, Ho Chi Minh City Viet Nam Master
1998 - 2002 Vietnam National University, Ho Chi Minh City Viet Nam Bachelor

Year Citation
2026 Lu, S., Liu, L., Yu, K., Le, T. D., Liu, J., & Li, J. (2026). Dependency-based anomaly detection: A general framework and comprehensive evaluation. Expert Systems with Applications, 297(129249), 129249.
DOI Scopus1 WoS1
2026 Cheng, D., Xu, Z., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2026). Linking model intervention to causal interpretation in model explanation. Pattern Recognition, 173, 112814-1-112814-12.
DOI Scopus2 WoS1
2026 Piñero, S. L., Duong, N., Li, X., Liu, L., Li, J., Sang, H., . . . Le, T. D. (2026). TACO: TabPFN Augmented Causal Outcomes for early detection of Long COVID. Machine Learning: Science and Technology, 7(3), 035029-1-035029-20.
DOI
2025 Amente, L. D., Mills, N. T., Le, T. D., Hyppönen, E., & Lee, S. H. (2025). A latent outcome variable approach for Mendelian randomization using the stochastic expectation maximization algorithm. Human Genetics, 144(5), 559-574.
DOI Scopus3 WoS3 Europe PMC1
2025 Amente, L. D., Mills, N. T., Le, T. D., Hypponen, E., & Lee, S. H. (2025). Disentangling horizontal and vertical Pleiotropy in genetic correlation estimation: introducing the HVP model. Human Genetics, 144(8), 861-876.
DOI Europe PMC1
2025 Pinero, S., Li, X., Zhang, J., Winter, M., Lee, S. H., Nguyen, T., . . . Le, T. D. (2025). Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID. Critical Reviews in Clinical Laboratory Sciences, 63(4), 332-358.
DOI Scopus2 WoS2
2025 Dao, B., Trinh, V. N., Nguyen, H. V., Nguyen, H. L., Le, T. D., & Luu, P. L. (2025). Crosstalk between genomic variants and DNA methylation in FLT3 mutant acute myeloid leukemia. Briefings in Functional Genomics, 24(elae028), 10 pages.
DOI Scopus1 WoS1 Europe PMC1
2025 Cheng, D., Li, J., Liu, L., Xu, Z., Zhang, W., Liu, J., & Le, T. D. (2025). Disentangled Representation Learning for Causal Inference With Instruments. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14078-14091.
DOI Scopus8 WoS8
2025 Li, X., Liu, L., Li, J., & Le, T. D. (2025). Stable Breast Cancer Prognosis. IEEE Transactions on Computational Biology and Bioinformatics, 22(2), 721-731.
DOI Scopus1
2025 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). Integrative multi-omics framework for causal gene discovery in Long COVID. PLoS Computational Biology, 21(12), e1013725-1-e1013725-32.
DOI
2025 Cifuentes Bernal, A. M., Liu, L., Li, J., & Le, T. D. (2025). Identifying cooperative genes causing cancer progression with dynamic causal inference. Royal Society Open Science, 12(12), 250442-1-250442-20.
DOI
2024 Amente, L. D., Mills, N. T., Le, T. D., Hyppönen, E., & Lee, S. H. (2024). Unraveling phenotypic variance in metabolic syndrome through multi-omics. Human Genetics, 143(1), 35-47.
DOI Scopus7 WoS7 Europe PMC3
2024 Zhang, J., Liu, L., Wei, X., Zhao, C., Luo, Y., Li, J., & Le, T. D. (2024). Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data. BMC Biology, 22(1), 19 pages.
DOI Scopus7 WoS7 Europe PMC6
2024 Cheng, D., Li, J., Liu, L., Yu, K., Duy Le, T., & Liu, J. (2024). Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data. IEEE Transactions on Neural Networks and Learning Systems, 35(8), 11542-11552.
DOI Scopus10 WoS8 Europe PMC2
2024 Cheng, D., Jiuyong, L. I., Liu, L., Liu, J., & Thuc Duy, L. E. (2024). Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey. ACM Computing Surveys, 56(5), 37 pages.
DOI Scopus45 WoS42
2024 Liu, J., Li, J., Liu, L., Le, T., Ye, F., & Li, G. (2024). Fairmod: making predictions fair in multiple protected attributes. Knowledge and Information Systems, 66(3), 1861-1884.
DOI Scopus3 WoS3
2023 Cheng, D., Li, J., Liu, L., Yu, K., Le, T. D., & Liu, J. (2023). Toward unique and unbiased causal effect estimation from data with hidden variables. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 1-13.
DOI Scopus25 WoS29 Europe PMC5
2023 Zhang, J., Liu, L., Wei, X., Zhao, C., Li, S., Li, J., & Le, T. D. (2023). Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers. Plos Computational Biology, 19(10 October), 28 pages.
DOI Scopus5 WoS4 Europe PMC5
2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2023). Personalized Interventions to Increase the Employment Success of People With Disability. IEEE Transactions on Big Data, 9(6), 1561-1574.
DOI Scopus1 WoS1
2023 Cheng, D., Li, J., Liu, L., Zhang, J., Liu, J., & Le, T. D. (2023). Local Search for Efficient Causal Effect Estimation. IEEE Transactions on Knowledge and Data Engineering, 35(9), 8823-8837.
DOI Scopus17 WoS16
2023 Li, J., Liu, L., Zhang, S., Ma, S., Le, T. D., & Liu, J. (2023). Causal heterogeneity discovery by bottom-up pattern search for personalised decision making. Applied Intelligence, 53(7), 8180-8194.
DOI
2022 Deho, O. B., Zhan, C., Li, J., Liu, J., Liu, L., & Le, T. D. (2022). How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?. British Journal of Educational Technology, 53(4), 822-843.
DOI Scopus70 WoS50
2022 Zhang, J., Liu, L., Zhang, W., Li, X., Zhao, C., Li, S., . . . Le, T. D. (2022). MiRspongeR 2.0: An enhanced R package for exploring miRNA sponge regulation. Bioinformatics Advances, 2(1), 3 pages.
DOI Scopus3 WoS2 Europe PMC5
2022 Cifuentes Bernal, A. M., Pham, V. V. H., Li, X., Liu, L., Li, J., & Le, T. D. (2022). Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories. Briefings in Functional Genomics, 21(6), 455-465.
DOI Scopus3 WoS4 Europe PMC3
2022 Li, X., Liu, L., Whitehead, C., Li, J., Thierry, B., Le, T. D., & Winter, M. (2022). Identifying preeclampsia-associated genes using a control theory method. Briefings in Functional Genomics, 21(4), 296-309.
DOI Scopus6 WoS7 Europe PMC9
2022 Zhang, J., Liu, L., Xu, T., Zhang, W., Li, J., Rao, N., & Le, T. D. (2022). Time to infer miRNA sponge modules. Wiley Interdisciplinary Reviews RNA, 13(2), 21 pages.
DOI Scopus20 WoS20 Europe PMC18
2022 Cheng, D., Li, J., Liu, L., Le, T. D., Liu, J., & Yu, K. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery, 36(3), 1174-1196.
DOI Scopus11 WoS10
2021 Pham, V. V. H., Liu, L., Bracken, C., Goodall, G., Li, J., & Le, T. D. (2021). Computational methods for cancer driver discovery: A survey. Theranostics, 11(11), 5553-5568.
DOI Scopus23 WoS24 Europe PMC20
2021 Pham, V. V. H., Liu, L., Bracken, C. P., Nguyen, T., Goodall, G. J., Li, J., & Le, T. D. (2021). pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers. Bioinformatics, 37(19), 3285-3292.
DOI Scopus14 WoS14 Europe PMC12
2021 Chaudhary, M. S., Pham, V. V. H., & Le, T. D. (2021). NIBNA: A network-based node importance approach for identifying breast cancer drivers. Bioinformatics, 37(17), 2521-2528.
DOI Scopus12 WoS13 Europe PMC4
2021 Nguyen, T., Le, H., Quinn, T. P., Nguyen, T., Le, T. D., & Venkatesh, S. (2021). GraphDTA: Predicting drug target binding affinity with graph neural networks. Bioinformatics, 37(8), 1140-1147.
DOI Scopus1087 WoS948 Europe PMC686
2021 Cifuentes-Bernal, A. M., Pham, V. V., Li, X., Liu, L., Li, J., & Le, T. D. (2021). A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes. Bioinformatics, 37(6), 807-814.
DOI Scopus7 WoS5 Europe PMC4
2021 Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2021). Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data. BMC Bioinformatics, 22(1), 19 pages.
DOI Scopus18 WoS15 Europe PMC17
2021 Nguyen, T., Lee, S. C., Quinn, T. P., Truong, B., Li, X., Tran, T., . . . Le, T. D. (2021). PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse. IEEE ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2841-2847.
DOI Scopus6 WoS5 Europe PMC5
2021 Li, X., Truong, B., Xu, T., Liu, L., Li, J., & Le, T. D. (2021). Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis. BMC Bioinformatics, 22(1), 22 pages.
DOI Scopus6 WoS6 Europe PMC3
2021 Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2021). miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data. RNA Biology, 18(12), 2308-2320.
DOI Scopus10 WoS7 Europe PMC9
2021 Pham, V. V. H., Li, X., Truong, B., Nguyen, T., Liu, L., Li, J., & Le, T. D. (2021). The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge. Briefings in Bioinformatics, 22(3), 4 pages.
DOI Scopus1 WoS2 Europe PMC4
2021 Li, J., Zhang, W., Liu, L., Yu, K., Le, T. D., & Liu, J. (2021). A general framework for causal classification. International Journal of Data Science and Analytics, 11(2), 127-139.
DOI Scopus6 WoS6
2021 Tarca, A. L., Pataki, B. Á., Romero, R., Sirota, M., Guan, Y., Kutum, R., . . . Sharma, R. (2021). Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Reports Medicine, 2(6), 100323.
DOI Scopus88 Europe PMC93
2020 Li, X., Liu, L., Goodall, G., Schreiber, A., Xu, T., Li, J., & Le, T. D. (2020). A novel single-cell based method for breast cancer prognosis.
DOI
2020 Li, X., Liu, L., Goodall, G. J., Schreiber, A., Xu, T., Li, J., & Le, T. D. (2020). A novel single-cell based method for breast cancer prognosis. PLoS Computational Biology, 16(8), e1008133-1-e1008133-20.
DOI Scopus23 WoS18 Europe PMC21
2020 Pham, V. V. H., Liu, L., Bracken, C. P., Goodall, G. J., Li, J., & Le, T. D. (2020). DriverGroup: a novel method for identifying driver gene groups. Bioinformatics, 36(Supplement_2), i583-i591.
DOI Scopus7 WoS8 Europe PMC6
2020 Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M. E., Zhang, X., . . . Meyer, P. (2020). Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data. Life Science Alliance, 3(11), 13 pages.
DOI Scopus17 WoS14 Europe PMC18
2020 Truong, B., Zhou, X., Shin, J., Li, J., van der Werf, J. H. J., Le, T. D., & Lee, S. H. (2020). Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives. Nature Communications, 11(1), 11 pages.
DOI Scopus24 WoS20 Europe PMC23
2020 Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. Plos Computational Biology, 16(4), e1007851.
DOI Scopus23 Europe PMC24
2020 Yu, K., Liu, L., Li, J., Ding, W., & Le, T. D. (2020). Multi-Source Causal Feature Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2240-2256.
DOI Scopus120 WoS99 Europe PMC9
2020 Li, J., Liu, L., Le, T. D., & Liu, J. (2020). Accurate data-driven prediction does not mean high reproducibility. Nature Machine Intelligence, 2(1), 13-15.
DOI Scopus45 WoS43
2020 Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLOS COMPUTATIONAL BIOLOGY, 16(4), 22 pages.
DOI WoS22
2020 Pan, J., Cui, T., Le, T. D., Li, X., & Zhang, J. (2020). Multi-group transfer learning on multiple latent spaces for text classification. IEEE Access, 8(9051683), 64120-64130.
DOI Scopus6 WoS4
2020 Zhang, J., Pham, V. V. H., Liu, L., Xu, T., Truong, B., Li, J., . . . Le, T. D. (2020). Correction: Identifying mirna synergism using multiple-intervention causal inference (BMC Bioinformatics (2019) 20 (613) DOI: 10.1186/s12859-019-3215-5). BMC Bioinformatics, 21(1), 2 pages.
DOI
2019 Pham, V. V., Zhang, J., Liu, L., Truong, B., Xu, T., Nguyen, T. T., . . . Le, T. D. (2019). Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction. BMC Bioinformatics, 20(1, article no. 143), 1-12.
DOI Scopus21 WoS23 Europe PMC22
2019 Pillman, K. A., Scheer, K. G., Hackett-Jones, E., Saunders, K., Bert, A. G., Toubia, J., . . . Bracken, C. P. (2019). Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon-Intron Split Analysis (EISA). Nucleic Acids Research, 47(16), 14 pages.
DOI Scopus10 WoS9 Europe PMC12
2019 Brown, P., Tan, A. -C., El-Esawi, M. A., Liehr, T., Blanck, O., Gladue, D. P., . . . Zhou, Y. (2019). Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database: the journal of biological databases and curation, 2019(baz085), baz085-1-baz085-66.
DOI Scopus33 WoS38 Europe PMC16
2019 Pham, V. V. H., Liu, L., Bracken, C. P., Goodall, G. J., Long, Q., Li, J., & Le, T. D. (2019). CBNA: a control theory based method for identifying coding and non-coding cancer drivers. PLoS computational biology, 15(12), e1007538.
DOI Scopus32 WoS31 Europe PMC26
2019 Zhang, J., Pham, V. V. H., Liu, L., Xu, T., Truong, B., Li, J., . . . Le, T. D. (2019). Identifying miRNA synergism using multiple-intervention causal inference. BMC Bioinformatics, 20(1), 11 pages.
DOI Scopus14 WoS14 Europe PMC18
2019 Choobdar, S., Ahsen, M. E., Crawford, J., Tomasoni, M., Fang, T., Lamparter, D., . . . Müller, F. (2019). Assessment of network module identification across complex diseases. Nature Methods, 16(9), 843-852.
DOI Scopus229 WoS226 Europe PMC248
2019 Zhang, J., Liu, L., Xu, T., Xie, Y., Zhao, C., Li, J., & Le, T. D. (2019). MiRspongeR: An R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics, 20(1), 12 pages.
DOI Scopus37 WoS30 Europe PMC31
2019 Ma, S., Li, J., Liu, L., & Le, T. D. (2019). Discovering context specific causal relationships. Intelligent Data Analysis, 23(4), 917-931.
DOI
2019 Ma, S., Liu, L., Li, J., & Le, T. D. (2019). Data-driven discovery of causal interactions. International Journal of Data Science and Analytics, 8(3), 285-297.
DOI Scopus2 WoS3
2019 Le, T. D., Zhao, Y., Wong, S., Jin, W. H., Ong, K. L., Liu, L., & Williams, G. (2019). Preface. Communications in Computer and Information Science, 1127 CCIS, v-vi.
2018 Thuyen, T., Suriyanarayanan, T., Zeng, G., Le, T. D., Liu, L., Li, J., . . . Seneviratne, C. J. (2018). Use of haploid model of Candida albicans to uncover mechanism of action of a novel antifungal agent. Frontiers In cellular and infection microbiology, 8(164), 1-14.
DOI Scopus15 WoS14 Europe PMC12
2018 Xu, T., Su, N., Liu, L., Zhang, J., Wang, H., Zhang, W., . . . Le, T. D. (2018). miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase. BMC Bioinformatics, 19(514), 1-10.
DOI Scopus63 WoS57 Europe PMC68
2018 Zhang, J., Liu, L., Li, J., & Le, T. D. (2018). LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer. Bioinformatics (Oxford, England), 34(24), 4232-4240.
DOI Scopus60 WoS55 Europe PMC55
2018 Le, T., Hoang, T., Li, J., Liu, L., Liu, H., & Hu, S. (2018). A fast PC algorithm for high dimensional causal discovery with multi-core PCs. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 1483-1495.
DOI Scopus125 WoS109 Europe PMC24
2018 Zhang, J., Le, T. D., Liu, L., & Li, J. (2018). Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer. Briefings in Bioinformatics, 20(4), 1403-1419.
DOI Scopus47 WoS43 Europe PMC42
2018 Zhang, W., Le, T. D., Liu, L., & Li, J. (2018). Estimating heterogeneous treatment effect by balancing heterogeneity and fitness. BMC Bioinformatics, 19(Suppl 19), 12 pages.
DOI Scopus5 WoS4 Europe PMC2
2017 Le, T. D., Zhang, J., Liu, L., & Li, J. (2017). Computational methods for identifying miRNA sponge interactions. Briefings in Bioinformatics, 18(4), 577-590.
DOI Scopus77 WoS68 Europe PMC70
2017 Liu, H., Liu, L., Le, T. D., Lee, I., Sun, S., & Li, J. (2017). Nonparametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction. IEEE Transactions on Multimedia, 19(8), 1848-1859.
DOI Scopus18 WoS18
2017 Xu, T., Le, T. D., Liu, L., Su, N., Wang, R., Sun, B., . . . Li, J. (2017). CancerSubtypes: An R/Bioconductor package for molecular cancer subtype identification, validation and visualization. Bioinformatics, 33(19), 3131-3133.
DOI Scopus203 WoS202 Europe PMC189
2017 Zhang, W., Le, T. D., Liu, L., Zhou, Z. H., & Li, J. (2017). Mining heterogeneous causal effects for personalized cancer treatment. Bioinformatics, 33(15), 2372-2378.
DOI Scopus38 WoS38 Europe PMC12
2017 Zhang, J., Le, T. D., Liu, L., & Li, J. (2017). Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer. BMC Bioinformatics, 18(1), 12 pages.
DOI Scopus23 WoS20 Europe PMC16
2017 Zhang, J., Le, T. D., Liu, L., & Li, J. (2017). Identifying miRNA sponge modules using biclustering and regulatory scores. BMC Bioinformatics, 18(Suppl 3), 12 pages.
DOI Scopus19 WoS17 Europe PMC12
2017 Li, J., Ma, S., Le, T., Liu, L., & Liu, J. (2017). Causal Decision Trees. IEEE transactions on knowledge and data engineering, 29(2), 257-271.
DOI Scopus60 WoS46
2016 Zhang, W., Le, T. D., Liu, L., Zhou, Z. H., & Li, J. (2016). Predicting miRNA targets by integrating gene Regulatory knowledge with Expression profiles. Plos One, 11(4), 19 pages.
DOI Scopus16 WoS23 Europe PMC11
2016 Xu, T., Le, T. D., Liu, L., Wang, R., Sun, B., & Li, J. (2016). Identifying cancer subtypes from miRNA-TFmRNA regulatory networks and expression data. Plos One, 11(4), 20 pages.
DOI Scopus66 WoS54 Europe PMC42
2016 Masud Karim, S. M., Liu, L., Le, T. D., & Li, J. (2016). Identification of miRNA-mRNA regulatory modules by exploring collective group relationships. BMC Genomics, 17(1), 14 pages.
DOI Scopus27 WoS22 Europe PMC17
2016 Zhang, J., Le, T. D., Liu, L., He, J., & Li, J. (2016). A novel framework for inferring condition-specific TF and miRNA co-regulation of protein-protein interactions. Gene, 577(1), 55-64.
DOI Scopus10 WoS11 Europe PMC8
2016 Zhang, J., Duy Le, T., Liu, L., He, J., & Li, J. (2016). Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs. Molecular Biosystems, 12(2), 454-463.
DOI Scopus11 WoS10 Europe PMC12
2016 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., Sun, B., & Ma, S. (2016). From observational studies to causal rule mining. ACM transactions on intelligent systems and technology, 7(2, article no. 14), 1-27.
DOI Scopus44 WoS16
2016 Ma, S., Li, J., Liu, L., & Le, T. D. (2016). Mining combined causes in large data sets. Knowledge Based Systems, 92, 104-111.
DOI Scopus19 WoS16
2015 Le, T. D., Zhang, J., Liu, L., Liu, H., & Li, J. (2015). miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships. PLOS One, 10(12), 1-15.
DOI Scopus31 WoS25 Europe PMC22
2015 Le, T. D., Zhang, J., Liu, L., & Li, J. (2015). Ensemble methods for miRNA target prediction from expression data. Plos One, 10(6), 19 pages.
DOI Scopus27 WoS21 Europe PMC23
2014 Zhang, J., Thuc, D., Liu, L., Liu, B., He, J., Goodall, G., & Li, J. (2014). Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data. Bioinformatics, 30(21), 3070-3077.
DOI Scopus23 WoS21 Europe PMC20
2014 Zhang, J., Thuc, D., Liu, L., Liu, B., He, J., Goodall, G., & Li, J. (2014). Identifying direct miRNA-mRNA causal regulatory relationships in heterogeneous data. Journal of Biomedical Informatics, 52, 438-447.
DOI Scopus27 WoS26 Europe PMC25
2014 Le, T. D., Liu, L., Zhang, J., Liu, B., & Li, J. (2014). From miRNA regulation to miRNA-TF co-regulation: Computational approaches and challenges. Briefings in Bioinformatics, 16(3), 475-496.
DOI Scopus41 WoS35 Europe PMC27
2013 Le, T., Liu, L., Liu, B., Tsykin, A., Goodall, G., Satou, K., & Li, J. (2013). Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics, 14(article no. 92), 1-13.
DOI Scopus39 WoS40 Europe PMC32
2013 Le, T., Liu, L., Tsykin, A., Goodall, G., Liu, B., Sun, B., & Li, J. (2013). Inferring microRNA-mRNA causal regulatory relationships from expression data. Bioinformatics, 29(6), 765-771.
DOI Scopus74 WoS72 Europe PMC52

Year Citation
2026 Vu, T., Le, T. D., Tran, H. X., Liu, L., Li, J., & Du, J. T. (2026). Causal Recommendation Method for Personalised Chemotherapy Optimisation in Breast Cancer. In Q. V. Nguyen, V. Li, P. Kwan, Y. Zhao, Y. L. Boo, & R. Nayak (Eds.), Communications in Computer and Information Science (Vol. 2765 CCIS, pp. 397-411). SPRINGER-VERLAG SINGAPORE PTE LTD.
DOI
2025 Gao, W., Xu, Z., Li, J., Liu, L., Liu, J., Le, T. D., . . . Chen, Y. (2025). TSI: A Multi-view Representation Learning Approach for Time Series Forecasting. In M. Gong, Y. Song, Y. S. Koh, W. Xiang, & D. Wang (Eds.), AI 2024: Advances in Artificial Intelligence:
37th Australasian Joint Conference on Artificial Intelligence, AI 2024 (Vol. 15442 LNAI, pp. 291-302). Singapore: SPRINGER-VERLAG SINGAPORE PTE LTD.
DOI Scopus4 WoS4
2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Recommending personalized interventions to increase employability of disabled jobseekers. In J. Gama (Ed.), Event/exhibition information: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, 16/05/2022-19/05/2022
Source details - Title: Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part III (Vol. 13282 LNAI, pp. 92-104). Switzerland: Springer.
DOI Scopus4 WoS2
2020 Zhang, J., Nguyen, T., Truong, B., Liu, L., Li, J., & Le, T. D. (2020). Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data. In X. Yang, C. D. Wang, M. S. Islam, & Z. Zhang (Eds.), Event/exhibition information: 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, Foshan, China, 12/11/2020-14/11/2020
Source details - Title: ADMA 2020: Advanced Data Mining and Applications (Vol. 12447 LNAI, pp. 395-409). Singapore: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI WoS1
2019 Le, T. D., Ong, K. L., Zhao, Y., Jin, W. H., Wong, S., Liu, L., & Williams, G. (2019). Preface - Data mining: AusDM: Australasian Conference on Data Mining. In Event/exhibition information: 17th Australasian Conference on Data Mining, AusDM 2019, Adelaide, Australia, 02/12/2019-05/12/2019
Source details - Title: Data mining: AusDM: Australasian Conference on Data Mining (pp. v-vi). Singapore: Springer Nature.
DOI

Year Citation
2025 Chen, X., Li, J., Liu, J., Liu, L., Peters, S., Le, T. D., . . . Walsh, A. (2025). Diffusion Models for Attribution. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 39 (pp. 2266-2274). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI
2025 Gao, W., Li, J., Cheng, D., Liu, L., Liu, J., Le, T., . . . Zhao, Y. (2025). Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction. In Ijcai International Joint Conference on Artificial Intelligence (pp. 9638-9646). Canada: International Joint Conferences on Artificial Intelligence Organization.
DOI Scopus2 WoS2
2025 Gao, W., Li, J., Liu, L., Le, T. D., Chen, X., Du, X., . . . Chen, Y. (2025). From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction. In I. Lynce, N. Murano, M. Vallati, S. Villata, F. Chesani, M. Milano, . . . M. Dastani (Eds.), In published processings need to add Proceedings of the 28th European Conference on Artificial Intelligence, including 14th Conference on Prestigious Applications of Intelligent Systems, as published in Frontiers in Artificial Intelligence and Applications Vol. 413 (pp. 1107-1114). Netherlands: IOS Press.
DOI
2025 Guo, X., Liu, L., Lu, S., Li, J., Le, T. D., & Liu, J. (2025). Can EEG Foundation Models Help with Epileptic Seizure Prediction?. In Proceedings of the IEEE International Conference on Big Data Bigdata (pp. 1924-1933). PEOPLES R CHINA, Macau: IEEE.
DOI
2024 Lin, L., Krubha, Y. S., Yang, Z., Ren, C., Le, T. D., Amerini, I., . . . Hu, S. (2024). Robust COVID-19 Detection in CT Images with CLIP. In Proceedings of the International Conference on Multimedia Information Processing and Retrieval Mipr (pp. 586-592). US: IEEE COMPUTER SOC.
DOI Scopus6 WoS2
2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., Gao, W., & Le, T. D. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the Aaai Conference on Artificial Intelligence Vol. 38 (pp. 11480-11488). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI Scopus19 WoS15
2024 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2024). Conditional instrumental variable regression with representation learning for causal inference. In 12th International Conference on Learning Representations, ICLR 2024 (pp. 1-17). US: International Conference on Learning Representations (ICLR).
Scopus9
2023 Cheng, D., Xu, Z., Li, J., Liu, L., Liu, J., & Le, T. D. (2023). Causal Inference with Conditional Instruments Using Deep Generative Models. In B. Williams, Y. Chen, & J. Neville (Eds.), Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023 Vol. 37 (pp. 7122-7130). US: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
DOI Scopus23 WoS18
2023 Le, T. D. (2023). The KDD'23 Workshop on Causal Discovery, Prediction and Decision (CDPD 2023). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 5865-5866). CA, Long Beach: ASSOC COMPUTING MACHINERY.
DOI
2023 Tran, H. X., Le, T. D., Li, J., Liu, L., Li, X., Liu, J., & Waters, T. (2023). Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4970-4980). US: ASSOC COMPUTING MACHINERY.
DOI Scopus1 WoS1
2023 Cheng, D., Xu, Z., Li, J., Liu, L., Le, T. D., & Liu, J. (2023). Learning Conditional Instrumental Variable Representation for Causal Effect Estimation. In D. Koutra, C. Plant, M. G. Rodriguez, E. Baralis, & F. Bonchi (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 14169 LNAI (pp. 525-540). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus7 WoS6
2023 Chen, X., Li, J., Liu, J., Peters, S., Liu, L., Le, T. D., & Walsh, A. (2023). Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation. In Proceedings 2023 IEEE International Conference on Big Data Bigdata 2023 (pp. 1064-1069). US: IEEE.
DOI
2023 Le, T. D., Li, J., Ness, R., Triantafillou, S., Shimizu, S., Cui, P., . . . Prosperi, M. (2023). Preface: The 2023 ACM SIGKDD workshop on causal discovery, prediction and decision. In T. Le (Ed.), Proceedings of Machine Learning Research Vol. 218 (pp. 1-2). Netherlands: ML Research Press.
2022 Le, T. D., Liu, L., Kiciman, E., Triantafyllou, S., & Liu, H. (2022). The KDD 2022 Workshop on Causal Discovery (CD2022). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4884-4885). US: ASSOC COMPUTING MACHINERY.
DOI
2022 Cheng, D., Li, J., Liu, L., Zhang, J., Le, T. D., & Liu, J. (2022). Ancestral instrument method for causal inference without complete knowledge. In L. Raedt (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (pp. 4843-4849). US: International Joint Conferences on Artificial Intelligence Organization.
DOI Scopus8 WoS3
2022 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). What is the most effective intervention to increase job retention for this disabled worker?. In KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3981-3991). US: ACM - Association for Computing Machinery Inc.
DOI Scopus4 WoS2
2022 Le, T. D., Liu, L., Kıcıman, E., Triantafyllou, S., & Liu, H. (2022). Preface: the 2022 ACM SIGKDD workshop on causal discovery. In Proceedings of Machine Learning Research Vol. 185 (pp. 1-2). US: ML Research Press.
2022 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2022). Decision Support for Disability Employment using Counterfactual Survival Analysis. In Proceedings 2022 IEEE International Conference on Big Data Big Data 2022 (pp. 2103-2112). US: IEEE.
DOI Scopus1
2021 Le, T. D., Li, J., Cooper, G., Triantafyllou, S., Bareinboim, E., Liu, H., & Kiyavash, N. (2021). The KDD 2021 Workshop on Causal Discovery (CD2021). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4141-4142). US: ASSOC COMPUTING MACHINERY.
DOI
2021 Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2021). Recommending the most effective intervention to improve employment for job seekers with disability. In KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3616-3626). US: Association for Computing Machinery.
DOI Scopus15 WoS12
2021 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2021). Divide and conquer: targeted adversary detection using proximity and dependency. In Z. Gong, X. Li, S. G. Oguducu, L. Chen, B. F. Manjon, & X. Wu (Eds.), Proceedings - 12th IEEE International Conference on Big Knowledge, ICBK 2021 (pp. 125-132). US: IEEE.
DOI Scopus1
2021 Le, T. D., Li, J., Cooper, G., Triantafdyllou, S., Bareinboim, E., Liu, H., & Kiyavash, N. (2021). Preface: the 2021 ACM SIGKDD workshop on causal discovery. In Proceedings of Machine Learning Research Vol. 150 (pp. 1-2). US: ML Research Press.
2020 Liu, J., Li, J., Ye, F., Liu, L., Le, T., Xiong, P., & Liu, H. C. (2020). Building fair predictive models. In M. Gallagher, N. Moustafa, & E. Lakshika (Eds.), 33rd Australasian Joint Conference on Artificial Intelligence, AI 2020 Proceedings Vol. 12576 LNAI (pp. 216-229). Switzerland: Springer Nature.
DOI Scopus1
2020 Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2020). LoPAD: A local prediction approach to anomaly detection. In H. W. Lauw, R. C. W. Wong, A. Ntoulas, E. P. Lim, S. K. Ng, & S. J. Pan (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12085 (pp. 660-673). Singapore: Springer.
DOI Scopus4 WoS3
2020 Xuan Tran, H., Duy Le, T., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2020). Intervention Recommendation for Improving Disability Employment. In X. T. Wu, C. Jermaine, L. Xiong, X. H. Hu, O. Kotevska, S. Y. Lu, . . . J. Saltz (Eds.), Proceedings 2020 IEEE International Conference on Big Data Big Data 2020 (pp. 1671-1680). US: IEEE.
DOI Scopus5 WoS5
2020 Cheng, D., Li, J., Liu, L., Liu, J., Yu, K., & Le, T. D. (2020). Causal query in observational data with hidden variables. In G. DeGiacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarin, & J. Lang (Eds.), Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 2551-2558). Netherlands: IOS PRESS.
DOI Scopus10 WoS7
2020 Nguyen, T., Nguyen, D. T., Le, T. D., & Venkatesh, S. (2020). MrPC: Causal Structure Learning in Distributed Systems. In H. Yang, K. Pasupa, A. C. S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Communications in Computer and Information Science Vol. 1332 (pp. 87-94). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI
2020 Le, T. D., Liu, L., Zhang, K., Kıcıman, E., Cui, P., & Hyvarinen, A. (2020). Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 127 (pp. 1-3). US: ML Research Press.
2019 Lu, S., Liu, L., Li, J., & Le, T. D. (2019). Effective outlier detection based on Bayesian Network and Proximity. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, . . . J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 134-139). US: IEEE.
DOI Scopus5 WoS4
2019 Le, T. D., Li, J., Zhang, K., Kıcıman, E., Cui, P., & Hyvärinen, A. (2019). Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 104 (pp. 1-3). US: ML Research Press.
Scopus1
2018 Le, T. D., Xu, T., Liu, L., Shu, H., Hoang, T., & Li, J. (2018). ParallelPC: An R package for efficient causal exploration in genomic data. In M. Ganji, L. Rashidi, B. C. M. Fung, & C. Wang (Eds.), Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 11154 LNAI (pp. 207-218). Switzerland: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus9 WoS3
2018 Le, T. D., Zhang, K., Kıcıman, E., Hyvarinen, A., & Liu, L. (2018). Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. In Proceedings of Machine Learning Research Vol. 92 (pp. 1-3). US: ML Research Press.
2017 Li, J., Liu, J., Liu, L., Le, T. D., Ma, S., & Han, Y. (2017). Discrimination detection by causal effect estimation. In J. Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, . . . M. Toyoda (Eds.), Proceedings 2017 IEEE International Conference on Big Data Big Data 2017 Vol. 2018-January (pp. 1087-1094). US: IEEE.
DOI Scopus12 WoS11
2017 Le, T. D., Zhang, J., Liu, L., Thanh Truong, B. M., Hu, S., Xu, T., & Li, J. (2017). Identifying microRNA targets in epithelial-mesenchymal transition using joint-intervention causal inference. In ACM International Conference Proceeding Series (pp. 34-41). US: ASSOC COMPUTING MACHINERY.
DOI Scopus2 WoS1
2016 Le, T. D., Hoang, T., Li, J., Liu, L., & Liu, H. (2016). A fast PC algorithm for high dimensional causal discovery for multi-core PCs. In International workshop on data mining in bioinformatics (pp. 1-10). US: IEEE.
2013 Li, J., Le, T. D., Liu, L., Liu, J., Jin, Z., & Sun, B. (2013). Mining causal association rules. In W. Ding, T. Washio, H. Xiong, G. Karypis, B. Thuraisingham, D. Cook, & X. Wu (Eds.), IEEE 13th International Conference on Data Mining Workshops 2013 proceedings (pp. 114-123). US: IEEE Press.
DOI Scopus42 WoS32
2012 Jin, Z., Li, J., Liu, L., Le, T. D., Sun, B., & Wang, R. (2012). Discovery of causal rules using partial association. In M. J. Zaki, A. Siebes, J. X. Yu, B. Goethals, G. Webb, & X. Wu (Eds.), Proceedings IEEE International Conference on Data Mining Icdm (pp. 309-318). US: IEEE.
DOI Scopus30 WoS24

Year Citation
2026 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., & Le, T. D. (2026). SPLIT: Safety Prioritization for Long COVID Drug Repurposing via a Causal Integrated Targeting Framework.
DOI
2025 Vu, T., Tran, H., Liu, L., Li, J., Du, J. T., & Le, T. (2025). Foundation Model-Based Recommendation of Optimal Neoadjuvant Therapy in Breast Cancer.
DOI
2025 Piñero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). TACO: TabPFN Augmented Causal Outcomes for Early Detection of Long COVID.
DOI
2025 Vu, T., Tran, H., Li, X., Liu, L., Li, J., Pinero, S., . . . Le, T. (2025). Tabular Foundation Model for Breast Cancer Prognosis using Gene Expression Data.
DOI
2025 Pinero, S., Li, X., Zhang, J., Winter, M., Lee, S. H., Nguyen, T., . . . Le, T. D. (2025). Omics-Based Computational Approaches for Biomarker Identification, Prediction, and Treatment of Long COVID.
DOI
2025 Pinero, S., Li, X., Liu, L., Li, J., Lee, S. H., Winter, M., . . . Le, T. D. (2025). Integrative Multi-Omics Framework for Causal Gene Discovery in Long COVID.
DOI
2025 Liu, X., Zhang, W., Tang, W., Le, T. D., Li, J., Liu, L., & Zhang, M. -L. (2025). From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification.
2024 Amente, L. D., Mills, N., Le, T. D., Hyppönen, E., & Lee, H. (2024). A latent outcome variable approach for Mendelian randomization using the expectation maximization algorithm.
DOI
2023 Cifuentes-Bernal, A., Liu, L., Li, J., & Le, T. D. (2023). Identifying cooperative genes causing cancer progression with dynamic causal inference.
DOI
2023 Zhang, J., Liu, L., Wei, X., Zhao, C., Luo, Y., Li, J., & Le, T. D. (2023). Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data.
DOI
2023 Zhang, J., Liu, L., Wei, X., Zhao, C., Li, S., Li, J., & Le, T. D. (2023). Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.
DOI
2021 Li, X., Liu, L., Li, J., & Le, T. (2021). Stable breast cancer prognosis.
DOI
2020 Cifuentes-Bernal, A., Pham, V. V. H., Li, X., Liu, L., Li, J., & Le, T. D. (2020). A Pseudo-Temporal Causality Approach to Identifying miRNA-mRNA Interactions During Biological Processes.
DOI
2020 Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2020). Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.
DOI
2020 Pham, V. V. H., Li, X., Truong, B., Nguyen, T., Liu, L., Li, J., & Le, T. (2020). The winning methods for predicting cellular position in the DREAM single cell transcriptomics challenge.
DOI
2020 Pham, V. V. H., Liu, L., Bracken, C., Nguyen, T., Goodall, G., Li, J., & Le, T. (2020). <i>pDriver</i> : A novel method for unravelling personalised coding and miRNA cancer drivers.
DOI
2020 Pham, V. V. H., Liu, L., Bracken, C., Goodall, G., Li, J., & Le, T. (2020). <i>DriverGroup</i> : A novel method for identifying driver gene groups.
DOI
2019 Zhang, J., Pham, V. V. H., Liu, L., Xu, T., Truong, B., Li, J., . . . Le, T. D. (2019). Identifying miRNA synergism using multiple-intervention causal inference.
DOI
2019 Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., . . . Le, T. D. (2019). LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer.
DOI
2019 Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M. E., Zhang, X., . . . Meyer, P. (2019). Predicting cellular position in the <i>Drosophila</i> embryo from Single-Cell Transcriptomics data.
DOI Europe PMC1
2019 Nguyen, T., Lee, S., Quinn, T., Truong, B., Li, X., Tran, T., . . . Le, T. D. (2019). PAN: Personalized Annotation-based Networks for the Prediction of Breast Cancer Relapse.
DOI
2018 Zhang, W., Le, T., Liu, L., & Li, J. (2018). Estimating heterogeneous treatment effects by balancing heterogeneity and fitness.
DOI
2018 Zhang, J., Liu, L., Xu, T., Xie, Y., Zhao, C., Li, J., & Le, T. D. (2018). miRsponge: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules.
DOI
2018 Xu, T., Su, N., Liu, L., Zhang, J., Wang, H., Zhang, W., . . . Le, T. D. (2018). miRBaseConverter: An R/Bioconductor Package for Converting and Retrieving miRNA Name, Accession, Sequence and Family Information in Different Versions of miRBase.
DOI
2018 Pham, V. V. H., Zhang, J., Liu, L., Truong, B. M. T., Xu, T., Nguyen, T. T., . . . Le, T. D. (2018). Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction.
DOI
  • Decarbonising concrete with halloysite, recycled plastic fibres and AI, ARC - Linkage, 01/01/2026 - 31/12/2028
  • Build competency aware and assuring machine learning systems, ARC - Discovery Projects, 01/01/2023 - 31/12/2026
  • Next generation causal inference methods for biological data, ARC - Discovery Early Career Researcher Award, 01/01/2020 - 31/12/2023
  • Maxima Training Group (Aust) Limited Scholarship, Maxima Training Group (Aust) Limited, 01/10/2019 - 30/06/2023
  • System biology approaches to uncovering non-coding RNAs' roles in characterising cancer&#xA;subtypes, NHMRC - Early Career Fellowship, 01/01/2017 - 31/12/2020

Courses I teach

  • INFS 3077 Text and Social Media Analytics (2025)
  • INFS 5144 Data Wrangling and Social Media Analytics (2025)
  • MATH 5045 Advanced Analytic Techniques (2025)
  • INFS 5144 Data Wrangling and Social Media Analytics (2024)
  • MATH 5045 Advanced Analytic Techniques (2024)

Date Role Research Topic Program Degree Type Student Load Student Name
2026 Principal Supervisor Utilizing AI to improve disease prediction and public health responses Doctor of Philosophy Doctorate Full Time Mr Hai Thanh Pham
2026 Co-Supervisor Enhancing Polygenic Risk Scores for Disease Risk Prediction in Clinical Applications Doctor of Philosophy Doctorate Full Time Mr Shahidul Islam
2025 Principal Supervisor Developing AI models for identifying the causes of Long COVID Doctor of Philosophy Doctorate Full Time Mr Xin Liu
2023 Principal Supervisor 109492- Developing causal-based methods for recommending the repurposed drugs for a disease and applications in breast cancer and SARS-CoV-2 Doctor of Philosophy Doctorate Full Time Mr Tuyen Vu
2023 Co-Supervisor Develop causality-based models for robust and adaptive drought predictions Doctor of Philosophy Doctorate Full Time Wentao Gao
2023 Co-Supervisor Transformer-based causal inference methods for temporal data with latent confounders Doctor of Philosophy Doctorate Full Time Mr Xudong Guo
2022 Co-Supervisor Novel statistical genomics methods for disentangling genetic architecture and causal pathways in cardiometabolic traits Doctor of Philosophy Doctorate Full Time Lamessa Amente
2022 Co-Supervisor Develop causal outcome prediction models for trustworthy interpretation Doctor of Philosophy Doctorate Full Time Xiongren Chen
2022 Principal Supervisor Computational methods for finding causal biomarkers and treatments for COVID-19 Doctor of Philosophy Doctorate Full Time Ms Sindy Pinero

Date Role Research Topic Program Degree Type Student Load Student Name
2020 - 2023 Co-Supervisor Investigating the fairness of predictive learning analytics models in static and dynamic environments Doctor of Philosophy Doctorate Full Time Mr Oscar Deho
2020 - 2022 Principal Supervisor Intervention recommendation for disability employment service Doctor of Philosophy Doctorate Full Time Mr Ha Tran
2019 - 2024 Principal Supervisor Dynamic causal inference methods for modelling biological processes in cancer Doctor of Philosophy Doctorate Full Time Mr Andres Cifuentes Bernal
2018 - 2022 Principal Supervisor Computational modelling for breast cancer prognosis in precision medicine Doctor of Philosophy Doctorate Full Time Ms Xiaomei Li
2018 - 2021 Principal Supervisor Developing network inference methods for cancer driver discovery Doctor of Philosophy Doctorate Full Time Mr Vu Viet Hoang Pham
2017 - 2021 Co-Supervisor Dependency-based anomaly detection and applications Doctor of Philosophy Doctorate Full Time Miss Sha Lu
2015 - 2018 Co-Supervisor Computational causal discovery from observational data and application in bioinformatics Doctor of Philosophy Doctorate Full Time Mr Weijia Zhang
2013 - 2017 Co-Supervisor Developing data mining methods for finding causal relationships involving multiple factors Doctor of Philosophy Doctorate Full Time Mr Saisai Ma

Date Role Board name Institution name Country
2017 - 2025 Co-Founder Data Analytics Group University of South Australia Australia

Date Role Membership Country
2015 - ongoing Member ABACBS -

Date Role Editorial Board Name Institution Country
2020 - ongoing Associate Editor BMC Cancer University of South Australia Australia
2020 - ongoing Associate Editor Plos One University of South Australia Australia

Date Engagement Type Partner Name
2019 - 2023 Research Contract Maxima Group

Date Title Type Institution Country
2017 - ongoing ARC, NHMRC, European Grant Councils Grant Assessment University of South Australia -

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