Causal Discovery
DEEP_causal
Causal heterogeneity discovery by bottom-up pattern search for personalised decision making, Appl Intell (2022)
TEDVAE: Treatment Effect Estimation with Disentangled Latent Factors
Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), (2021).
DAVS-Q: Towards unique and unbiased causal effect estimation from data with hidden variables
arXiv:2002.10091v2 [stat.ME], (2020).
M3B: Mining Markov Blankets Without Causal Sufficiency
IEEE Transactions on Neural Networks and Learning Systems,Volume: 29, Issue: 12 (2018).
MCFS: Multi-Source Causal Feature selection
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 42, Issue: 9 (2019).
SurvivalCausalTree: Mining heterogeneous causal effects for personalized cancer treatment
Bioinformatics, Volume 33, Issue 15, (2017)
ParallelPC: Paralellised Versions of Constraint Based Causal Discovery Algorithms.
The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.
Software for the book: "Practical approaches to causal relationship exploration"
Website with tutorials and examples.
Causal Decision Tree implementation
IEEE Transactions on Knowledge and Data Engineering, Volume: 29, Issue: 2, (2017).
Discrimination detection by combining association rules and potential outcome model
Bioinformatics
CancerSubtypesPrognosis R package contains functions to predict breast cancer subtypes and prognosis.
BMC Bioinformatics 22, 300 (2021).
pDriver: A novel method for unravelling personalised coding and miNRA cancer drivers
Bioinformatics, Volume 37, Issue 19, (2021)
NIBNA: a network-based node importance approach for identifying breast cancer drivers
Bioinformatics, Volume 37, Issue 17, (2021)
A pseudotemporal causality approach to identifying miRNA–mRNA interactions during biological processes
Bioinformatics, Volume 37, Issue 6, (2021)
DriverGroup: a novel method for identifying driver gene groups
Bioinformatics, Volume 36, Issue Supplement_2 (2020)
scPrognosis: A novel single-cell based method for breast cancer prognosis
PLoS Comput Biol 16(8) (2020)
LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer
PLoS Comput Biol 16(4) (2020)
miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules.
BMC Bioinformatics 20, 235 (2019).
CBNA: A control theory based method for identifying coding and non-coding cancer drivers
PLoS Comput Biol 15(12) (2019)
miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules
BMC Bioinformatics volume 20, Article number: 235 (2019)
miRsyn: A novel method for identifying miRNA synergism using multiple-intervention causal inference.
BMC Bioinformatics volume 20, Article number: 613 (2019)
miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions.
BMC Bioinformatics, 19(s19), 514.
MSLCRN: a novel method for inferring module-specific lncRNA-mRNA causal regulatory network in human cancer.
Briefings in Bioinformatics, Volume 20, Issue 4, (2019)
LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer.
Bioinformatics, Volume 34, Issue 24, (2018)
CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.
Bioinformatics, Volume 33, Issue 19, 01 October 2017, Pages 3131–3133.
"miRLAB": Dry lab for exploring miRNA-mRNA relationships
PLoS ONE, 10(12), e0145386.
Data Mining
Statistically non-redundant Subgroup Discovery
Knowledge-Based Systems, Volume 67, (2014)
Conditional Functional Dependency Discovery
The Computer Journal, Volume 56, Issue 3, (2013)
Diversified Multiple Tree Classifier
Intelligent Systems For Bioinformatics (2006)
Robust rule based classifications
Conferences in Research and Practice in Information Technology. Vol. 39. Australian Computer Society Inc., (2005).
Mining risk and preventive patterns
KDD’05, August 21–24, (2005)