Dr Thuc Le

Senior Lecturer

Data Analytics Group
School of Information Technology and Mathematical Sciences
University of South Australia


Email: Thuc.Le@UNI, where UNI=unisa.edu.au
Phone: +61 8 830 23996
Post: School of Information Technology and Mathematical Sciences, UniSA, Mawson Lakes, SA 5095, Australia
Office: D-3-14, Mawson Lakes Campus, Mawson Lakes Blv.


I am currently a DECRA Fellow (2020-2022), and before that, an NHMRC ECR Fellow in Bioinformatics/Computational Biology (2017-2019). Bioinformatics is an inter-disciplinary research area which uses knowledge in Computer Science, Mathematics, and Statistics to solve biological problems. My research focuses on the development of causal inference methods and their applications in Bioinformatics, particularly in gene regulatory networks, cancer drivers, non-coding RNAs, and cancer subtype discovery. I have a diverse educational background with BSc and MSc in Mathematics, BSc in Computer Science, and PhD in Data Science. I have been awarded the Ian Davey Thesis Prize for the most outstanding PhD thesis at UniSA, a visiting researcher at the University of Michigan in 2015, and a visiting professor at the University of Pennsylvania in 2019. This report long, short summaries my research in the last few years. My CV , Google Scholar , Research Gate , Home Page .

Services (selected)

Grants (selected)



  1. miRLAB, Homepage in Bioconductor
  2. CancerSubtypes, Homepage in Bioconductor
  3. miRSpongeR, Homepage in Bioconductor
  4. miRBaseConverter, Homepage in Bioconductor
  5. ParallelPC, Homepage in CRAN
  6. Software for the book: "Practical approaches to causal relationship exploration", Causal Book



  1. Truong, B., Zhou, X., Jisu, S., Li, J., Van der Werf, J.H.J, Le, T.D, Lee, S.H. Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives. Nature Communications, 2020. pdf
  2. Cheng, D., Li, J., Liu, L., Liu, J., Yu, K. and Le, T.D. Causal query in observational data with hidden variables. ECML, 2020
  3. Lu, S., Liu, L., Li, J., Le, T.D. and Liu, J., LoPAD: A Local Prediction Approach to Anomaly Detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2020
  4. Pan, J., Cui, T., Le, T.D., Li, X. and Zhang, J. Multi-Group Transfer Learning on Multiple Latent Spaces for Text Classification. IEEE Access, 2020.
  5. Zhang, J., Xu, T., Liu, L., Zhang, W., Zhao, C., Li, S., Li, J., Rao, N. and Le, T.D. LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLoS computational biology, 2020.
  6. J Li, L Liu, TD Le, J Liu, Accurate data-driven prediction does not mean high reproducibility. Nature Machine Intelligence, 2020. pdf


  1. VVH Pham, L Liu, CP Bracken, GJ Goodall, Q Long, J Li, TD Le, CBNA: A control theory based method for identifying coding and non-coding cancer drivers. PLoS Computational Biology. pdf
  2. J Zhang, L Liu, T Xu, Y Xie, C Zhao, J Li, TD Le, miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics, 2019. pdf
  3. J Zhang, VVH Pham, L Liu, T Xu, B Truong, J Li, N Rao, TD Le, Identifying miRNA synergism using multiple-intervention causal inference. BMC Bioinformatics, 2019. pdf
  4. VVH Pham, J Zhang, L Liu, B Truong, T Xu, TT Nguyen, J Li, TD Le, Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction. BMC Bioinformatics, 2019. pdf
  5. S Ma, L Liu, J Li, TD Le, Data-driven discovery of causal interactions. International Journal of Data Science and Analytics, 2019. pdf
  6. Katherine A Pillman et al., Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon–Intron Split Analysis (EISA). Nucleic Acids Research, 2019. pdf
  7. Dream Challenge, Assessment of network module identification across complex diseases. Nature Methods, 2019. pdf
  8. Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kıcıman, Peng Cui, Aapo Hyvärinen, Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. Proceedings of Machine Learning Research, 2019. pdf
  9. J Zhang, TD Le, L Liu, J Li, Inferring and analyzing module-specific lncRNA–mRNA causal regulatory networks in human cancer. Briefings in bioinformatics, 2019. pdf
  10. S Ma, J Li, L Liu, TD Le, Discovering context specific causal relationships. Intelligent Data Analysis, 2019. pdf
  11. Brown, P., et al. Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database, 2019.
  12. K Yu, L Liu, J Li, W Ding, TD Le, Multi-source causal feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. pdf


  1. J Zhang, L Liu, J Li, TD Le, LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer. Bioinformatics, 2018. pdf
  2. T Xu, N Su, L Liu, J Zhang, H Wang, W Zhang, J Gui, K Yu, J Li, TD Le, miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase. BMC Bioinformatics, 2018. pdf
  3. W Zhang, TD Le, L Liu, J Li, Estimating heterogeneous treatment effect by balancing heterogeneity and fitness. BMC bioinformatics 2018. pdf
  4. TD Le, T Xu, L Liu, H Shu, T Hoang, J Li, ParallelPC: an R package for efficient causal exploration in genomic data, Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018. pdf
  5. Truong, T., Suriyanarayanan, T., Zeng, G., Le, T.D., Liu, L., Li, J., Tong, C., Wang, Y. and Seneviratne, C.J.. Use of haploid model of Candida albicans to uncover mechanism of action of a novel antifungal agent. Frontiers in cellular and infection microbiology, 2018.
  6. S Lu, L Liu, J Li, TD Le, Effective Outlier Detection based on Bayesian Network and Proximity. IEEE International Conference on Big Data (Big Data), 2018
  7. TD Le, A dry lab for exploring miRNA functions and applications in cancer subtype discovery. The third Asia-Pacific Bioconductor Meeting, 2018. pdf


  1. T Xu, T Duy Le, L Liu, N Su, R Wang, B Sun, A Colaprico, G Bontempi, J Li, CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation, and visualization, Bioinformatics, 2017. pdf
  2. J Zhang, TD Le, L Liu, J Li, Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer, BMC Bioinformatics, 2017. pdf
  3. W Zhang, T Duy Le, L Liu, ZH Zhou, J Li, Mining heterogeneous causal effects for personalized cancer treatment, Bioinformatics, 2017. pdf
  4. H Liu, L Liu, TD Le, I Lee, S Sun, J Li, Non-Parametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction, IEEE Transactions on Multimedia, 2017. pdf
  5. J Zhang, TD Le, L Liu, J Li, Identifying miRNA sponge modules using biclustering and regulatory scores, BMC Bioinformatics, 2017. pdf
  6. J Li, S Ma, TD Le, L Liu, J Liu, Causal Decision Trees, TKDE, 2017. pdf
  7. TD Le, T Hoang, J Li, L Liu, H Liu, A fast PC algorithm for high dimensional causal discovery with multi-core PCs, ACM/IEEE TCBB 2017. pdf
  8. TD Le, J Zhang, L Liu, J Li, Computational methods for identifying miRNA sponge interactions. Briefings in bioinformatics, 2017. pdf
  9. J Li, J Liu, L Liu, TD Le, S Ma, Y Han, Discrimination detection by causal effect estimation. IEEE International Conference on Big Data (Big Data), 2017. pdf
  10. Le, T.D., Zhang, J., Liu, L., Truong, B.M.T., Hu, S., Xu, T. and Li, J. Identifying microrna targets in epithelial-mesenchymal transition using joint-intervention causal inference. In Proceedings of the 8th International Conference on Computational Systems-Biology and Bioinformatics, 2017.


  1. W Zhang, TD Le, L Liu, ZH Zhou, J Li, Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles. Plos One, 2016. pdf
  2. T Xu, TD Le, L Liu, R Wang, B Sun, J Li, Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data. Plos One, 2016. pdf
  3. J Zhang, TD Le, L Liu, J He, J Li, A novel framework for inferring condition-specific TF and miRNA co-regulation of protein–protein interactions. Gene, 2016. pdf
  4. J Li, TD Le, L Liu, J Liu, Z Jin, B Sun, S Ma, From observational studies to causal rule mining. ACM TIST, 2016. pdf
  5. S Ma, J Li, L Liu, TD Le, Mining combined causes in large data sets. KBS, 2016. pdf
  6. SMM Karim, L Liu, TD Le, J Li, Identification of miRNA-mRNA regulatory modules by exploring collective group relationships. BMC Genomics, 2016. pdf
  7. J Zhang, TD Le, L Liu, J He, J Li, Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs. Molecular BioSystems, 2016. pdf
  8. TD Le, J Zhang, L Liu, J Li, Ensemble Methods for MiRNA Target Prediction from Expression Data. Plos One, 2016. pdf


  1. J Li, L Liu, T Le, Practical approaches to causal relationship exploration. Springer, 2015. Link to the book
  2. TD Le, J Zhang, L Liu, H Liu, J Li, miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships. Plos One, 2015. pdf
  3. TD Le, L Liu, J Zhang, B Liu, J Li, From miRNA regulation to miRNA–TF co-regulation: computational approaches and challenges. Briefings in Bioinformatics, 2015. pdf


  1. J Zhang, TD Le, L Liu, B Liu, J He, GJ Goodall, J Li, Identifying direct miRNA–mRNA causal regulatory relationships in heterogeneous data. Journal of Biomedical Informatics, 2014. pdf
  2. J Zhang, TD Le, L Liu, B Liu, J He, GJ Goodall, J Li, Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data. Bioinformatics, 2014. pdf


  1. TD Le, L Liu, A Tsykin, GJ Goodall, B Liu, BY Sun, J Li, Inferring microRNA–mRNA causal regulatory relationships from expression data. Bioinformatics, 2013. pdf
  2. TD Le, L Liu, B Liu, A Tsykin, GJ Goodall, K Satou, J Li, Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics, 2013. pdf
  3. J Li, TD Le, L Liu, J Liu, Z Jin, B Sun, Mining causal association rules. ICDM, Causality Workshop, 2013. pdf
  4. Z Jin, J Li, L Liu, TD Le, B Sun, R Wang, Discovery of causal rules using partial association. ICDM, 2012. pdf

Preprints (under review) and Working Papers

  1. Li, J., Zhang, W., Liu, L., Yu, K., Le, T. and Liu, J., 2020. A general framework for causal classification. pdf .
  2. Cheng, D., Li, J., Liu, L., Yu, K., Lee, T.D. and Liu, J., 2020. Towards precise causal effect estimation from data with hidden variables. pdf .
  3. Nguyen, T., Le, H., Quinn, T., Le, T., and Venkatesh, S., 2019. GraphDTA: prediction of drug–target binding affinity using graph convolutional networks. pdf .
  4. Tarca, A.L., Pataki, B.Á., Romero, R., Sirota, M., Guan, Y., Kutum, R., Gomez-Lopez, N., Done, B., Bhatti, G., Yu, T. and Andreoletti, G., 2020. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. pdf .
  5. Pham, V.V.H., Li, X., Truong, B., Nguyen, T., Liu, L., Li, J. and Le, T., 2020. The winning methods for predicting cellular position in the DREAM single cell transcriptomics challenge. pdf .
  6. Li, X., Liu, L., Goodall, G., Schreiber, A.W., Xu, T., Li, J. and Le, T., 2020. A novel single-cell based method for breast cancer prognosis. pdf .
  7. Pham, V.V.H., Liu, L., Bracken, C., Goodall, G., Li, J. and Le, T., 2020. DriverGroup: A novel method for identifying driver gene groups. pdf .
  8. Pham, V.V.H., Liu, L., Bracken, C., Nguyen, T., Goodall, G., Li, J. and Le, T., 2020. pDriver: A novel method for unravelling personalised coding and miRNA cancer drivers. pdf .
  9. Li, J., Ma, S., Liu, L., Le, T.D., Liu, J. and Han, Y., 2019. Identify treatment effect patterns for personalised decisions. pdf .
  10. Tanevski, J., Nguyen, T., Truong, B., Karaiskos, N., Ahsen, M.E., Zhang, X., Shu, C., Hu, Y., Pham, H.V., Li, X. and Le, T.D., 2019. Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data. pdf .
  11. Nguyen, T., Lee, S.C., Quinn, T.P., Truong, B., Li, X., Tran, T., Venkatesh, S. and Le, T.D., 2019. Personalized Annotation-based Networks (PAN) for the Prediction of Breast Cancer Relapse. pdf .
  12. Liu, J., Li, J., Ye, F., Liu, L., Le, T.D. and Xiong, P., 2018. An exploration of algorithmic discrimination in data and classification. pdf .
  13. Liu, J., Li, J., Liu, L., Le, T.D., Ye, F. and Li, G., 2018. FairMod-Making Predictive Models Discrimination Aware. pdf .
  14. Working paper. MrPC: Causal structure learning in distributed systems . pdf .
  15. Working paper. Group-based causal inference methods for identifying cooperative microRNA-mRNA regulatory relationships in EMT. pdf
Thuc Le