Dr Thuc Le

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 senior lecturer in Data Science and a National Health & Medical Research Council (NHMRC) Early Career Fellow (2017-2020) in Bioinformatics. Bioinformatics is an inter-disciplinary research area which uses knowledge in Mathematics, Statistics, and Computer Science to solve biological problems. I have a diverse educational background with BSc and MSc in Mathematics, BSc in Computer Science, and PhD in Bioinformatics. I have been awarded the Ian Davey Thesis Prize for the most outstanding PhD thesis at UniSA, Early Career Researcher Networking Awards to visit University of Michigan, USA, in 2015 and University of Pennsylvania in 2019. My research focuses on the development of causal inference methods and their applications in Bioinformatics, particularly in gene regulatory networks and cancer subtype discovery. This report Causal inference methods and applications in Bioinformatics summaries my research in the last five years. For more information, please visit my: My CV , Google Scholar , Research Gate , Home Page .

Services (selected)

Grants (selected)



  1. miRLAB, Homepage in Bioconductor
  2. CancerSubtypes, Homepage in Bioconductor
  3. miRSponge, 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. J Li, L Liu, T Le, Practical approaches to causal relationship exploration. Springer, 2015.

Journal Articles

  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, 2016. pdf
  9. 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
  10. 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
  11. 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
  12. 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
  13. S Ma, J Li, L Liu, TD Le, Mining combined causes in large data sets. KBS, 2016. pdf
  14. SMM Karim, L Liu, TD Le, J Li, Identification of miRNA-mRNA regulatory modules by exploring collective group relationships. BMC Genomics, 2016. pdf
  15. 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
  16. TD Le, J Zhang, L Liu, J Li, Ensemble Methods for MiRNA Target Prediction from Expression Data. Plos One, 2016. pdf
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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


  1. TD Le, A dry lab for exploring miRNA functions and applications in cancer subtype discovery. The third Asia-Pacific Bioconductor Meeting, 2018. pdf
  2. J Li, TD Le, L Liu, J Liu, Z Jin, B Sun, Mining causal association rules. ICDM, Causality Workshop, 2013. pdf
  3. Z Jin, J Li, L Liu, TD Le, B Sun, R Wang, Discovery of causal rules using partial association. ICDM, 2012. pdf
Thuc Le