Dr Thuc Le

Associate professor

Data Analytics Group
UniSA STEM
University of South Australia

Contact

Email: thuc.le@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.

Introduction

I am currently an Associate Professor at UniSA STEM. I was 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 Causal inference and machine learning applications in Bioinformatics summaries my research in the last few years. My CV , Google Scholar , Research Gate , Home Page .

Services (selected)

Grants (selected)

Publications

Software

  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

Books, Journals, and Conferences

2024

  1. Zhang, Junpeng, Lin Liu, Xuemei Wei, Chunwen Zhao, Yanbi Luo, Jiuyong Li, and Thuc Duy Le. "Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data." bioRxiv (2024): 2023-08. pdf
  2. Cifuentes Bernal, Andres, Lin Liu, Jiuyong Li, and Thuc Le. "Identifying cooperative genes causing cancer progression with dynamic causal inference." bioRxiv (2024): 2023-11. pdf
  3. Li, Jiuyong, Lin Liu, Ziqi Xu, Ha Xuan Tran, Thuc Duy Le, and Jixue Liu. "Linking a predictive model to causal effect estimation." arXiv preprint arXiv:2304.04566 (2023). pdf
  4. Cheng, Debo, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, and Thuc Duy Le. "Conditional Instrumental Variable Regression with Representation Learning for Causal Inference." ICLR 2024. pdf
  5. Liu, Jixue, Jiuyong Li, Lin Liu, Thuc Le, Feiyue Ye, and Gefei Li. "Fairmod: making predictions fair in multiple protected attributes." Knowledge and Information Systems (2023): 1-24. pdf
  6. Cheng, Debo, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Wentao Gao, and Thuc Duy Le. "Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders." AAAI (2024).
  7. Amente, Lamessa Dube, Natalie T. Mills, Thuc Duy Le, Elina Hyppönen, and S. Hong Lee. "Unraveling phenotypic variance in metabolic syndrome through multi-omics." Human Genetics (2023): 1-13.

2023

  1. Zhang, Junpeng, Lin Liu, Xuemei Wei, Chunwen Zhao, Yanbi Luo, Jiuyong Li, and Thuc Duy Le. "Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data." bioRxiv (2023): 2023-08. pdf
  2. Cifuentes Bernal, Andres, Lin Liu, Jiuyong Li, and Thuc Le. "Identifying cooperative genes causing cancer progression with dynamic causal inference." bioRxiv (2023): 2023-11. pdf
  3. Li, Jiuyong, Lin Liu, Shisheng Zhang, Saisai Ma, Thuc Duy Le, and Jixue Liu. "Causal heterogeneity discovery by bottom-up pattern search for personalised decision making." Applied Intelligence 53, no. 7 (2023): 8180-8194. pdf
  4. Cheng, Debo, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, and Jixue Liu. "Discovering ancestral instrumental variables for causal inference from observational data." IEEE Transactions on Neural Networks and Learning Systems (2023). pdf
  5. Li, Jiuyong, Lin Liu, Ziqi Xu, Ha Xuan Tran, Thuc Duy Le, and Jixue Liu. "Linking a predictive model to causal effect estimation." arXiv preprint arXiv:2304.04566 (2023). pdf
  6. Cheng, Debo, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, and Jixue Liu. "Learning Conditional Instrumental Variable Representation for Causal Effect Estimation." arXiv preprint arXiv:2306.12453 (2023). pdf
  7. Cheng, Debo, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, and Thuc Duy Le. "Causal inference with conditional instruments using deep generative models." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 6, pp. 7122-7130. 2023. pdf
  8. Tran, Ha Xuan, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, and Tony Waters. "Personalized Interventions to Increase the Employment Success of People with Disability." IEEE Transactions on Big Data (2023). pdf
  9. Le, Thuc Duy. "The KDD'23 Workshop on Causal Discovery, Prediction and Decision (CDPD 2023)." In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 5865-5866. 2023. pdf
  10. Tran, Ha Xuan, Thuc Duy Le, Jiuyong Li, Lin Liu, Xiaomei Li, Jixue Liu, and Tony Waters. "Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments." In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4970-4980. 2023. pdf
  11. Zhang, Junpeng, Lin Liu, Xuemei Wei, Chunwen Zhao, Sijing Li, Jiuyong Li, and Thuc Duy Le. "Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers." PLOS Computational Biology 19, no. 10 (2023): e1011308. pdf

2022

  1. Zhang, Junpeng, Lin Liu, Wu Zhang, Xiaomei Li, Chunwen Zhao, Sijing Li, Jiuyong Li, and Thuc Duy Le. "miRspongeR 2.0: an enhanced R package for exploring miRNA sponge regulation." Bioinformatics Advances 2, no. 1 (2022): vbac063. pdf
  2. Cheng, Debo, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, and Jixue Liu. "Toward unique and unbiased causal effect estimation from data with hidden variables." IEEE Transactions on Neural Networks and Learning Systems (2022). pdf
  3. Cheng, Debo, Jiuyong Li, Lin Liu, Jiji Zhang, and Jixue Liu. "Ancestral instrument method for causal inference without a causal graph." arXiv preprint arXiv:2201.03810 (2022). pdf
  4. Zhang, Junpeng, Lin Liu, Taosheng Xu, Wu Zhang, Jiuyong Li, Nini Rao, and Thuc Duy Le. "Time to infer miRNA sponge modules." Wiley Interdisciplinary Reviews: RNA 13, no. 2 (2022): e1686. pdf
  5. Cheng, Debo, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu, and Kui Yu. "Sufficient dimension reduction for average causal effect estimation." Data Mining and Knowledge Discovery 36, no. 3 (2022): 1174-1196. pdf
  6. Tran, Ha Xuan, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, and Tony Waters. "Recommending personalized interventions to increase employability of disabled jobseekers." In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 92-104. Cham: Springer International Publishing, 2022. pdf
  7. Li, Jiuyong, Ha Xuan Tran, Thuc Duy Le, Lin Liu, Kui Yu, and Jixue Liu. "Explanatory causal effects for model agnostic explanations." arXiv preprint arXiv:2206.11529 (2022). pdf
  8. Li, Xiaomei, Lin Liu, Clare Whitehead, Jiuyong Li, Benjamin Thierry, Thuc D. Le, and Marnie Winter. "Identifying preeclampsia-associated genes using a control theory method." Briefings in Functional Genomics 21, no. 4 (2022): 296-309. pdf
  9. Deho, Oscar Blessed, Chen Zhan, Jiuyong Li, Jixue Liu, Lin Liu, and Thuc Duy Le. "How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?." British Journal of Educational Technology 53, no. 4 (2022): 822-843. pdf
  10. Tran, Ha Xuan, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, and Tony Waters. "What is the most effective intervention to increase job retention for this disabled worker?." In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3981-3991. 2022. pdf
  11. Cheng, Debo, Jiuyong Li, Lin Liu, Jixue Liu, and Thuc Duy Le. "Data-driven causal effect estimation based on graphical causal modelling: A survey." ACM Computing Surveys (2022). pdf
  12. Cheng, Debo, Jiuyong Li, Lin Liu, Jiji Zhang, Jixue Liu, and Thuc Duy Le. "Local search for efficient causal effect estimation." IEEE Transactions on Knowledge and Data Engineering (2022). pdf
  13. Tran, Ha Xuan, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, and Tony Waters. "Decision Support for Disability Employment using Counterfactual Survival Analysis." In 2022 IEEE International Conference on Big Data (Big Data), pp. 2103-2112. IEEE, 2022. pdf

2021

  1. X Li, B Truong, T Xu, L Liu, J Li, TD Le, Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis, BMC bioinformatics 22 (1), 2021 pdf
  2. VVH Pham, L Liu, CP Bracken, T Nguyen, GJ Goodall, J Li, TD Le, pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers, Bioinformatics 37 (19), 2021 link
  3. MS Chaudhary, VVH Pham, TD Le, P Robinson, NIBNA: a network-based node importance approach for identifying breast cancer drivers, BMC bioinformatics 22 (1), 1-22, 2021 link
  4. J Zhang, L Liu, T Xu, W Zhang, J Li, N Rao, TD Le, Time to infer miRNA sponge modules, Wiley Interdisciplinary Reviews: RNA, e1686, 2021 pdf
  5. AL Tarca, B? Pataki, R Romero, M Sirota, Y Guan, R Kutum, ..., TD Le, ..., Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth, Cell Reports Medicine 2 (6), 2021 link
  6. VVH Pham, X Li, B Truong, T Nguyen, L Liu, J Li, TD Le, The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge, Briefings in bioinformatics 22 (3), 2021 link
  7. T Nguyen, S Lee, T Quinn, B Truong, X Li, T Tran, S Venkatesh, TD Le, PAN: Personalized Annotation-based Networks for the Prediction of Breast Cancer Relapse, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021 link
  8. T Nguyen, H Le, TP Quinn, T Nguyen, TD Le, S Venkatesh, GraphDTA: Predicting drug?target binding affinity with graph neural networks, Bioinformatics 37 (8), 2021 link
  9. J Zhang, L Liu, T Xu, W Zhang, C Zhao, S Li, J Li, N Rao, TD Le, miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data, RNA biology, 2021 link
  10. AM Cifuentes-Bernal, VV Pham, X Li, L Liu, J Li, TD Le, A pseudotemporal causality approach to identifying miRNA?mRNA interactions during biological processes, Bioinformatics 37 (6), 2021 link
  11. J Li, W Zhang, L Liu, K Yu, TD Le, J Liu, A general framework for causal classification, International Journal of Data Science and Analytics, 2021 link
  12. VVH Pham, L Liu, C Bracken, G Goodall, J Li, TD Le, Computational methods for cancer driver discovery: A survey, Theranostics 11 (11), 2021 pdf
  13. TD Le, J Li, G Cooper, S Triantafyllou, E Bareinboim, H Liu, N Kiyavash, The KDD 2021 Workshop on Causal Discovery (CD2021), Proceedings KDD, 2021 pdf
  14. HX Tran, TD Le, J Li, L Liu, J Liu, Y Zhao, T Waters, Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability, Proceedings KDD, 2021 pdf

2020

  1. VVH Pham, L Liu, CP Bracken, GJ Goodall, J Li, TD Le, DriverGroup: a novel method for identifying driver gene groups, Bioinformatics, 2020 pdf
  2. Jovan Tanevski;Thin Nguyen;Buu Truong, ..., Hoang Vv Pham;Li Xiaomei;Thuc D Le, ..., Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data, Life science alliance, 2020 pdf
  3. X Li, L Liu, GJ Goodall, A Schreiber, T Xu, J Li, TD Le, A novel single-cell based method for breast cancer prognosis, PLoS computational biology 16 (8), 2020 pdf
  4. B Truong, X Zhou, J Shin, J Li, JHJ van der Werf, TD Le, SH Lee, Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives, Nature communications, 2020 pdf
  5. J Zhang, T Xu, L Liu, W Zhang, C Zhao, S Li, J Li, N Rao, TD Le, LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer, PLoS computational biology, 2020 pdf
  6. J Pan, T Cui, TD Le, X Li, J Zhang, Multi-Group Transfer Learning on Multiple Latent Spaces for Text Classification, IEEE Access, 2020 pdf
  7. J Li, L Liu, TD Le, J Liu, Accurate data-driven prediction does not mean high reproducibility, Nature Machine Intelligence, 2020 pdf
  8. HX Tran, TD Le, J Li, L Liu, J Liu, Y Zhao, T Waters, Intervention Recommendation for Improving Disability Employment, IEEE International Conference on Big Data, 2020 link
  9. T Nguyen, DT Nguyen, TD Le, S Venkatesh, MrPC: Causal Structure Learning in Distributed Systems, International Conference on Neural Information Processing link
  10. J Zhang, T Nguyen, B Truong, L Liu, J Li, TD Le, Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data, International Conference on Advanced Data Mining and Applications link
  11. S Lu, L Liu, J Li, TD Le, J Liu, LoPAD: A Local Prediction Approach to Anomaly Detection, Advances in Knowledge Discovery and Data Mining. PAKDD 2020 pdf

2019

  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, 2019 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. KA Pillman, KG Scheer, E Hackett-Jones, K Saunders, AG Bert, J Toubia, ..., TD Le, ..., Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon?Intron Split Analysis (EISA), Nucleic acids research, 2019 pdf
  7. S Choobdar, ME Ahsen, J Crawford, M Tomasoni, T Fang, D Lamparter, ..., TD Le, ..., Assessment of network module identification across complex diseases, Nature methods, 2019 pdf
  8. 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
  9. 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
  10. P Brown, AC Tan, MA El-Esawi, T Liehr, O Blanck, DP Gladue, ..., TD Le, ..., Large expert-curated database for benchmarking document similarity detection in biomedical literature search, Database, 2019 pdf
  11. S Ma, J Li, L Liu, TD Le, Discovering context specific causal relationships, Intelligent Data Analysis, 2019 link
  12. Thuc D. Le, Kok-Leong Ong, Yanchang Zhao, Warren H. Jin, Sebastien Wong, Lin ..., Data Mining - 17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2-5, 2019, Proceedings, Springer, 2019 link
  13. TD Le, J Li, K Zhang, E K?c?man, P Cui, A Hyv䲩nen, Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery, Proceedings of Machine Learning Research, 2019 pdf

2018

  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. T Truong, T Suriyanarayanan, G Zeng, TD Le, L Liu, J Li, C Tong, Y Wang, ..., Use of haploid model of Candida albicans to uncover mechanism of action of a novel antifungal agent, Frontiers in cellular and infection microbiology, 2018 pdf
  5. J Zhang, L Liu, J Li, TD Le, Effective outlier detection based on Bayesian network and proximity, 2018 IEEE international conference on big data (big data), 2018 link
  6. 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
  7. TD Le, A dry lab for exploring miRNA functions and applications in cancer subtype discovery. The third Asia-Pacific Bioconductor Meeting, 2018. pdf

2017

  1. 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
  2. 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
  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. J Li, J Liu, L Liu, TD Le, S Ma, Y Han, Discrimination detection by causal effect estimation, 2017 IEEE International Conference on Big Data (Big Data), 2017 pdf
  9. TD Le, J Zhang, L Liu, BMT Truong, S Hu, T Xu, J Li, Identifying microrna targets in epithelial-mesenchymal transition using joint-intervention causal inference, Proceedings of the 8th International Conference on Computational Systems, 2017 pdf

2016

  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. S Ma, J Li, L Liu, TD Le, Mining combined causes in large data sets. KBS, 2016. pdf
  5. SMM Karim, L Liu, TD Le, J Li, Identification of miRNA-mRNA regulatory modules by exploring collective group relationships. BMC Genomics, 2016. pdf
  6. 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
  7. 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
  8. TD Le, J Zhang, L Liu, J Li, Ensemble Methods for MiRNA Target Prediction from Expression Data. Plos One, 2016. pdf
  9. D Le, J Zhang, L Liu, J Li, Computational methods for identifying miRNA sponge interactions. Briefings in bioinformatics, 2016. pdf

2015

  1. 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
  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. J Li, L Liu, T Le, Practical approaches to causal relationship exploration. Springer, 2015.

2014

  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

2013 and Earlier

  1. 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
  2. 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
  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
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