ORCID Profile
0000-0003-2843-5738
Current Organisation
University of South Australia
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Bioinformatics Software | Medical Devices | Machine learning not elsewhere classified | Central Nervous System | Data mining and knowledge discovery | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Information and Communication Services not elsewhere classified | Computer Software and Services not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences | Health and Support Services not elsewhere classified |
Related Links
Publications
Privacy preserving serial publication of transactional data
Publisher: Elsevier BV
Date: 05-2019
Multi-label relational classification via node and label correlation
Publisher: Elsevier BV
Date: 05-2018
Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data
Publisher: Elsevier BV
Date: 12-2020
Identifying miRNA synergism using multiple-intervention causal inference
Publisher: Springer Science and Business Media LLC
Date: 12-2019
DOI: 10.1186/S12859-019-3215-5
Abstract: Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they in idually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.
A coloured petri net approach to the functional and performance analysis of SIP non-INVITE transaction
Publisher: Springer Berlin Heidelberg
Date: 2014
Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data
Publisher: Springer Science and Business Media LLC
Date: 10-10-2017
miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships
Publisher: Public Library of Science (PLoS)
Date: 30-12-2015
Use of Haploid Model of Candida albicans to Uncover Mechanism of Action of a Novel Antifungal Agent
Publisher: Frontiers Media SA
Date: 08-06-2018
Guest Editorial: Special Issue on Causal Discovery 2017
Publisher: Springer Science and Business Media LLC
Date: 08-2018
Measurement Invariance of the Self-Description Questionnaire II in a Chinese Sample
Publisher: Hogrefe Publishing Group
Date: 04-2016
DOI: 10.1027/1015-5759/A000242
Abstract: Abstract. Studies on the construct validity of the Self-Description Questionnaire II (SDQII) have not compared the factor structure between the English and Chinese versions of the SDQII. By using rigorous multiple group comparison procedures based upon confirmatory factor analysis (CFA) of measurement invariance, the present study examined the responses of Australian high school students (N = 302) and Chinese high school students (N = 322) using the English and Chinese versions of the SDQII, respectively. CFA provided strong evidence that the factor structure (factor loading and item intercept) of the Chinese version of the SDQII in comparison to responses to the English version of the SDQII is invariant, therefore it allows researchers to confidently utilize both the English and Chinese versions of the SDQII with Chinese and Australian s les separately and cross-culturally.
Learning Markov Blankets From Multiple Interventional Data Sets
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Forensic taxonomy of Android social apps
Publisher: Wiley
Date: 28-11-2017
Abstract: An Android social app taxonomy incorporating artifacts that are of forensic interest will enable users and forensic investigators to identify the personally identifiable information (PII) stored by the apps. In this study, 30 popular Android social apps were examined. Artifacts of forensic interest (e.g., contacts lists, chronology of messages, and timest of an added contact) were recovered. In addition, images were located, and Facebook token strings used to tie account identities and gain access to information entered into Facebook by a user were identified. Based on the findings, a two-dimensional taxonomy of the forensic artifacts of the social apps is proposed. A comparative summary of existing forensic taxonomies of different categories of Android apps, designed to facilitate timely collection and analysis of evidentiary materials from Android devices, is presented.
Fuzzy Bayesian networks-a general formalism for representation, inference and learning with hybrid Bayesian networks
Publisher: IEEE
Medical Applications of Artificial Intelligence
Publisher: CRC Press
Date: 06-11-2013
DOI: 10.1201/B15618
An Android Social App Forensics Adversary Model
Publisher: IEEE
Date: 2016
FUZZY BAYESIAN NETWORKS — A GENERAL FORMALISM FOR REPRESENTATION, INFERENCE AND LEARNING WITH HYBRID BAYESIAN NETWORKS
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2000
DOI: 10.1142/S021800140000060X
Abstract: This paper proposes a general formalism for representation, inference and learning with general hybrid Bayesian networks in which continuous and discrete variables may appear anywhere in a directed acyclic graph. The formalism fuzzifies a hybrid Bayesian network into two alternative forms: the first form replaces each continuous variable in the given directed acyclic graph (DAG) by a partner discrete variable and adds a directed link from the partner discrete variable to the continuous one. The mapping between two variables is not crisp quantization but is approximated (fuzzified) by a conditional Gaussian (CG) distribution. The CG model is equivalent to a fuzzy set but no fuzzy logic formalism is employed. The conditional distribution of a discrete variable given its discrete parents is still assumed to be multinomial as in discrete Bayesian networks. The second form only replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adds a directed link from that partner discrete variable to the continuous one. The dependence between the partner discrete variable and the original continuous variable is approximated by a CG distribution, but the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution. Obviously, the second form is a finer approximation, but restricted to CGR models, and requires more complicated inference and learning algorithms. This results in two general approximate representations of a general hybrid Bayesian networks, which are called here the fuzzy Bayesian network (FBN) form-I and form-II. For the two forms of FBN, general exact inference algorithms exists, which are extensions of the junction tree inference algorithm for discrete Bayesian networks. Learning fuzzy Bayesian networks from data is different from learning purely discrete Bayesian networks because not only all the newly converted discrete variables are latent in the data, but also the number of discrete states for each of these variables and the CG or CGR distribution of each continuous variable given its partner discrete parents or both continuous and discrete parents have to be determined.
Personalized Interventions to Increase the Employment Success of People With Disability
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis
Publisher: Springer Science and Business Media LLC
Date: 04-06-2021
DOI: 10.1186/S12859-021-04215-3
Abstract: Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.
Multilabel Feature Selection: A Local Causal Structure Learning Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy
Publisher: Springer Science and Business Media LLC
Date: 12-2009
Local Search for Efficient Causal Effect Estimation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Estimating biological parameters for penaeid restocking in an Australian temperate estuary
Publisher: Elsevier BV
Date: 02-2017
A data-driven method to detect adverse drug events from prescription data
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.JBI.2018.07.013
Abstract: Drug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient s les for discovering common ADEs. However, more s les are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈D
Mining combined causes in large data sets
Publisher: Elsevier BV
Date: 2016
Accurate data-driven prediction does not mean high reproducibility
Publisher: Springer Science and Business Media LLC
Date: 17-01-2020
Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction
Publisher: Springer Science and Business Media LLC
Date: 15-03-2019
Adaptive Skeleton Construction for Accurate DAG Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
From miRNA regulation to miRNA-TF co-regulation: computational approaches and challenges
Publisher: Oxford University Press (OUP)
Date: 12-07-2014
DOI: 10.1093/BIB/BBU023
Abstract: microRNAs (miRNAs) are important gene regulators. They control a wide range of biological processes and are involved in several types of cancers. Thus, exploring miRNA functions is important for diagnostics and therapeutics. To date, there are few feasible experimental techniques for discovering miRNA regulatory mechanisms. Alternatively, predictions of miRNA-mRNA regulatory relationships by computational methods have increasingly achieved promising results. Computational approaches are proving their ability as effective tools in reducing the number of biological experiments that must be conducted and to assist with the design of the experiments. In this review, we categorize and review different computational approaches to identify miRNA activities and functions, including the co-regulation of miRNAs and transcription factors. Our main focuses are on the recent approaches that use multiple data types for exploring miRNA functions. We discuss the remaining challenges in the evaluation and selection of models based on the results from a case study. Finally, we analyse the remaining challenges of each computational approach and suggest some future research directions.
Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Publisher: Springer Science and Business Media LLC
Date: 02-12-2021
DOI: 10.1186/S12859-021-04498-6
Abstract: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to in idual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of in idual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell–cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell–cell crosstalk. To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.
CBNA: A control theory based method for identifying coding and non-coding cancer drivers
Publisher: Public Library of Science (PLoS)
Date: 02-12-2019
pDriver : A novel method for unravelling personalised coding and miRNA cancer drivers.
Publisher: Oxford University Press (OUP)
Date: 27-04-2021
DOI: 10.1093/BIOINFORMATICS/BTAB262
Abstract: Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at in idual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at in idual level. We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. pDriver is available at vvhoang Driver. Supplementary data are available at Bioinformatics online.
Computational methods for identifying miRNA sponge interactions
Publisher: Oxford University Press (OUP)
Date: 05-06-2016
DOI: 10.1093/BIB/BBW042
Abstract: Recent findings show that coding genes are not the only targets that miRNAs interact with. In fact, there is a pool of different RNAs competing with each other to attract miRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The ceRNAs indirectly regulate each other via the titration mechanism, i.e. the increasing concentration of a ceRNA will decrease the number of miRNAs that are available for interacting with other targets. The cross-talks between ceRNAs, i.e. their interactions mediated by miRNAs, have been identified as the drivers in many disease conditions, including cancers. In recent years, some computational methods have emerged for identifying ceRNA-ceRNA interactions. However, there remain great challenges and opportunities for developing computational methods to provide new insights into ceRNA regulatory mechanisms.In this paper, we review the publically available databases of ceRNA-ceRNA interactions and the computational methods for identifying ceRNA-ceRNA interactions (also known as miRNA sponge interactions). We also conduct a comparison study of the methods with a breast cancer dataset. Our aim is to provide a current snapshot of the advances of the computational methods in identifying miRNA sponge interactions and to discuss the remaining challenges.
Discovering Functional microRNA-mRNA Regulatory Modules in Heterogeneous Data
Publisher: Springer Netherlands
Date: 19-12-2012
DOI: 10.1007/978-94-007-5590-1_14
Abstract: microRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translation inhibition. They are pivotal regulators of development and cellular homeostasis through their control of erse processes. Recently, great efforts have been made to elucidate many targets that are affected by miRNAs, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles and sequence information to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. In this chapter, we first briefly review the computational methods for discovering miRNA targets and miRNA-mRNA regulatory modules, and then focus on a method of identifying functional miRNA-mRNA regulatory modules by integrating multiple data sets from different sources.
Causal Feature Selection With Dual Correction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Which Type of Classifier to Use for Networked Data, Connectivity Based or Feature Based?
Publisher: Springer International Publishing
Date: 2018
Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study
Publisher: Elsevier BV
Date: 2022
A general framework for causal classification
Publisher: Springer Science and Business Media LLC
Date: 03-2021
Causal learner: A toolbox for causal structure and Markov blanket learning
Publisher: Elsevier BV
Date: 11-2022
Android mobile VoIP apps: a survey and examination of their security and privacy
Publisher: Springer Science and Business Media LLC
Date: 21-11-2016
Multi-Source Causal Feature Selection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Assessment of network module identification across complex diseases
Publisher: Springer Science and Business Media LLC
Date: 30-08-2019
A knowledge-based data fusion strategies for airborne multisensor surveillance system
Publisher: IEEE
Learning Causal Representations for Robust Domain Adaptation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Nonparametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Identifying functional miRNA–mRNA regulatory modules with correspondence latent dirichlet allocation
Publisher: Oxford University Press (OUP)
Date: 17-10-2010
DOI: 10.1093/BIOINFORMATICS/BTQ576
Abstract: Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of erse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. Results: We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules. Contact: bing.liu@unisa.edu.au jiuyong.li@unisa.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
Modelling of money laundering and terrorism financing typologies
Publisher: Emerald
Date: 06-07-2012
DOI: 10.1108/13685201211238061
Abstract: The purpose of this paper is to show how modelling can be used to provide an easy‐to‐follow, visual representation of the important characteristics and aspects of money laundering behaviours extracted from real‐world money laundering and terrorism financing typologies. In total, 184 typologies were obtained from a number of anti‐money laundering and counter‐terrorism financing (AML/CTF) bodies to determine the common patterns and themes present in the cases involved. Financial flows, transactions and interactions between entities were extracted from each of the typologies and modelled using the Unified Modelling Language (UML) features within Microsoft Visio. The paper demonstrates how complex transactional flows and interactions between the different entities involved in a money laundering and terrorism financing case can be shown in an easy‐to‐follow graphical representation, allowing practitioners to more easily and quickly extract the relevant information from the typology, as opposed to reading a full text‐based description. In addition, these models make it easier to discover trends and patterns present within and across Types and allow money laundering and terrorism financing typologies to be updated and published to the wider international AML/CTF community, as and when new trends and behaviours become apparent. A set of models have been produced that can be extended every time a new scenario, typology or Type arises. These models can be held in a central repository that can be added to and updated by AML/CTF practitioners and can be referred to by practitioners to help them identify whether the case that they are dealing with fits already predefined money laundering and terrorism financing behaviours, or whether a new behaviour has been discovered. These models may also be useful for the development of money laundering and terrorism financing detection tools and the training of new analysts or practitioners. The authors believe that their work goes some way to addressing the current lack of formal methods and techniques for identifying and developing uniform procedures for describing, classifying and sharing new money laundering and terrorism financing with the international AML/CTF community, as and when they happen, in a simple but effective manner.
Uncovering SIP Vulnerabilities to DoS Attacks Using Coloured Petri Nets
Publisher: IEEE
Date: 11-2011
FastOPM—A practical method for partial match of time series
Publisher: Elsevier BV
Date: 10-2022
Time to infer miRNA sponge modules
Publisher: Wiley
Date: 03-08-2021
DOI: 10.1002/WRNA.1686
Abstract: Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer‐related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer‐associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan‐cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules Small Molecule‐RNA Interactions Regulatory RNAs/RNAi/Riboswitches Regulatory RNAs
Reducing Parametric Automata: A Multimedia Protocol Service Case Study
Publisher: Springer Berlin Heidelberg
Date: 2004
A Fast PC Algorithm for High Dimensional Causal Discovery with Multi-Core PCs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Inferring microRNA–mRNA causal regulatory relationships from expression data
Publisher: Oxford University Press (OUP)
Date: 30-01-2013
DOI: 10.1093/BIOINFORMATICS/BTT048
Abstract: Motivation: microRNAs (miRNAs) are known to play an essential role in the post-transcriptional gene regulation in plants and animals. Currently, several computational approaches have been developed with a shared aim to elucidate miRNA–mRNA regulatory relationships. Although these existing computational methods discover the statistical relationships, such as correlations and associations between miRNAs and mRNAs at data level, such statistical relationships are not necessarily the real causal regulatory relationships that would ultimately provide useful insights into the causes of gene regulations. The standard method for determining causal relationships is randomized controlled perturbation experiments. In practice, however, such experiments are expensive and time consuming. Our motivation for this study is to discover the miRNA–mRNA causal regulatory relationships from observational data. Results: We present a causality discovery-based method to uncover the causal regulatory relationship between miRNAs and mRNAs, using expression profiles of miRNAs and mRNAs without taking into consideration the previous target information. We apply this method to the epithelial-to-mesenchymal transition (EMT) datasets and validate the computational discoveries by a controlled biological experiment for the miR-200 family. A significant portion of the regulatory relationships discovered in data is consistent with those identified by experiments. In addition, the top genes that are causally regulated by miRNAs are highly relevant to the biological conditions of the datasets. The results indicate that the causal discovery method effectively discovers miRNA regulatory relationships in data. Although computational predictions may not completely replace intervention experiments, the accurate and reliable discoveries in data are cost effective for the design of miRNA experiments and the understanding of miRNA–mRNA regulatory relationships. Availability: The R scripts are in the Supplementary material. Contact: thuc_duy.le@mymail.unisa.edu.au or jiuyong.li@unisa.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase
Publisher: Springer Science and Business Media LLC
Date: 12-2018
Should Learning Analytics Models Include Sensitive Attributes? Explaining the Why
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Stabilising Job Survival Analysis for Disability Employment Services in Unseen Environments
Publisher: ACM
Date: 04-08-2023
DriverGroup: A novel method for identifying driver gene groups
Publisher: Oxford University Press (OUP)
Date: 12-2020
DOI: 10.1093/BIOINFORMATICS/BTAA797
Abstract: Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on in idual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. DriverGroup is available at vvhoang/DriverGroup Supplementary data are available at Bioinformatics online.
Are the financial transactions conducted inside virtual environments truly anonymous?: An experimental research from an Australian perspective
Publisher: Emerald
Date: 28-12-2013
Ensemble Methods for MiRNA Target Prediction from Expression Data
Publisher: Public Library of Science (PLoS)
Date: 26-06-2015
Symbolic Language Representations for Parametric Verification of the Revised Capability Exchange Signalling Protocol
Publisher: IEEE
Date: 12-2007
From Observational Studies to Causal Rule Mining
Publisher: Association for Computing Machinery (ACM)
Date: 24-11-2015
DOI: 10.1145/2746410
Abstract: Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an alternative to RCTs. However, in observational studies, prior knowledge is required to generate the hypotheses about the cause-effect relationships to be tested, and hence they can only be applied to problems with available domain knowledge and a handful of variables. In practice, many datasets are of high dimensionality, which leaves observational studies out of the opportunities for causal discovery from such a wealth of data sources. In another direction, many efficient data mining methods have been developed to identify associations among variables in large datasets. The problem is that causal relationships imply associations, but the reverse is not always true. However, we can see the synergy between the two paradigms here. Specifically, association rule mining can be used to deal with the high-dimensionality problem, whereas observational studies can be utilised to eliminate noncausal associations. In this article, we propose the concept of causal rules (CRs) and develop an algorithm for mining CRs in large datasets. We use the idea of retrospective cohort studies to detect CRs based on the results of association rule mining. Experiments with both synthetic and real-world datasets have demonstrated the effectiveness and efficiency of CR mining. In comparison with the commonly used causal discovery methods, the proposed approach generally is faster and has better or competitive performance in finding correct or sensible causes. It is also capable of finding a cause consisting of multiple variables—a feature that other causal discovery methods do not possess.
Tackling the Infinite State Space of a Multimedia Control Protocol Service Specification
Publisher: Springer Berlin Heidelberg
Date: 2002
Discrimination detection by causal effect estimation
Publisher: IEEE
Date: 12-2017
ParallelPC: An R Package for Efficient Causal Exploration in Genomic Data
Publisher: Springer International Publishing
Date: 2018
Training Neural Networks with Random Noise Images for Adversarial Robustness
Publisher: ACM
Date: 26-10-2021
LoPAD: A Local Prediction Approach to Anomaly Detection
Publisher: Springer International Publishing
Date: 2020
Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data
Publisher: Oxford University Press (OUP)
Date: 23-07-2014
DOI: 10.1093/BIOINFORMATICS/BTU489
Abstract: Motivation: MicroRNAs (miRNAs) play crucial roles in complex cellular networks by binding to the messenger RNAs (mRNAs) of protein coding genes. It has been found that miRNA regulation is often condition-specific. A number of computational approaches have been developed to identify miRNA activity specific to a condition of interest using gene expression data. However, most of the methods only use the data in a single condition, and thus, the activity discovered may not be unique to the condition of interest. Additionally, these methods are based on statistical associations between the gene expression levels of miRNAs and mRNAs, so they may not be able to reveal real gene regulatory relationships, which are causal relationships. Results: We propose a novel method to infer condition-specific miRNA activity by considering (i) the difference between the regulatory behavior that an miRNA has in the condition of interest and its behavior in the other conditions (ii) the causal semantics of miRNA–mRNA relationships. The method is applied to the epithelial–mesenchymal transition (EMT) and multi-class cancer (MCC) datasets. The validation by the results of transfection experiments shows that our approach is effective in discovering significant miRNA–mRNA interactions. Functional and pathway analysis and literature validation indicate that the identified active miRNAs are closely associated with the specific biological processes, diseases and pathways. More detailed analysis of the activity of the active miRNAs implies that some active miRNAs show different regulation types in different conditions, but some have the same regulation types and their activity only differs in different conditions in the strengths of regulation. Availability and implementation: The R and Matlab scripts are in the Supplementary materials . Contact: jiuyong.li@unisa.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
Identifying microRNA targets in epithelial-mesenchymal transition using joint-intervention causal inference
Publisher: ACM
Date: 07-12-2017
A simple yet effective data integration approach to tree-based microarray data classification
Publisher: IEEE
Date: 08-2010
Data-driven discovery of causal interactions
Publisher: Springer Science and Business Media LLC
Date: 12-01-2019
Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data
Publisher: Public Library of Science (PLoS)
Date: 04-2016
Identifying direct miRNA–mRNA causal regulatory relationships in heterogeneous data
Publisher: Elsevier BV
Date: 12-2014
DOI: 10.1016/J.JBI.2014.08.005
Abstract: Discovering the regulatory relationships between microRNAs (miRNAs) and mRNAs is an important problem that interests many biologists and medical researchers. A number of computational methods have been proposed to infer miRNA-mRNA regulatory relationships, and are mostly based on the statistical associations between miRNAs and mRNAs discovered in observational data. The miRNA-mRNA regulatory relationships identified by these methods can be both direct and indirect regulations. However, differentiating direct regulatory relationships from indirect ones is important for biologists in experimental designs. In this paper, we present a causal discovery based framework (called DirectTarget) to infer direct miRNA-mRNA causal regulatory relationships in heterogeneous data, including expression profiles of miRNAs and mRNAs, and miRNA target information. DirectTarget is applied to the Epithelial to Mesenchymal Transition (EMT) datasets. The validation by experimentally confirmed target databases suggests that the proposed method can effectively identify direct miRNA-mRNA regulatory relationships. To explore the upstream regulators of miRNA regulation, we further identify the causal feedforward patterns (CFFPs) of TF-miRNA-mRNA to provide insights into the miRNA regulation in EMT. DirectTarget has the potential to be applied to other datasets to elucidate the direct miRNA-mRNA causal regulatory relationships and to explore the regulatory patterns.
Verification of the Capability Exchange Signalling protocol
Publisher: Springer Science and Business Media LLC
Date: 17-03-2007
Mining Causal Association Rules
Publisher: IEEE
Date: 12-2013
Spectral Representation of Protein Sequences
Publisher: American Scientific Publishers
Date: 07-2011
The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge
Publisher: Oxford University Press (OUP)
Date: 25-08-2021
DOI: 10.1093/BIB/BBAA181
Abstract: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the ex le use cases are available in the Supplementary data.
Recursive Parametric Automata and ε-Removal
Publisher: Springer Berlin Heidelberg
Date: 2009
Identifying miRNA sponge modules using biclustering and regulatory scores
Publisher: Springer Science and Business Media LLC
Date: 03-2017
A Unified View of Causal and Non-causal Feature Selection
Publisher: Association for Computing Machinery (ACM)
Date: 18-04-2021
DOI: 10.1145/3436891
Abstract: In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.
A novel framework for inferring condition-specific TF and miRNA co-regulation of protein–protein interactions
Publisher: Elsevier BV
Date: 02-2016
DOI: 10.1016/J.GENE.2015.11.023
Abstract: Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independently regulate their downstream targets, collaborate with each other to regulate gene expression. However, their synergistic roles in protein-protein interactions (PPIs) remain mostly unknown. In this paper, we present a novel framework (called CoRePPI) for inferring TF and miRNA co-regulation of PPIs. Particularly, CoRePPI is aimed at discovering the co-regulation specific to a condition of interest, by using heterogeneous data, including miRNA and messenger RNA (mRNA) expression profiles, putative miRNA targets, TF targets and PPIs. CoRePPI firstly finds the network motifs indicating the co-regulation of PPIs by TFs and miRNAs in tumor and normal conditions separately. Then by identifying the differential motifs found in one condition but not in the other, it builds the networks consisting of TFs, miRNAs and their co-regulated PPIs specific to different conditions respectively. To validate CoRePPI, we apply it to the Pan-Cancer dataset which includes the expression profiles of 12 cancer types from TCGA. Through network topology analysis, we found that the tumor and normal CoRePPI networks are scale-free. Furthermore, the results of differential and intersected network analysis between the tumor and normal CoRePPI networks suggest that only a small fraction of the regulatory relationships between TFs and miRNAs are conserved in both conditions but they co-regulate different downstream PPIs in tumor and normal conditions and in different conditions the majority of the regulatory relationships between TFs and miRNAs are different although they may regulate the same PPIs in their respective conditions. The CoRePPI sub-networks constructed for the three types of cancers (breast cancer, lung cancer and ovarian cancer) are all scale-free, and the intersection of these CoRePPI sub-networks can be utilized as the biomarker CoRePPI sub-network of the three types of cancers. The PPI enrichment analyses of the tumor and normal CoRePPI networks suggest that the co-regulating TFs and miRNAs are significantly associated with the specific biological processes, diseases and pathways. In addition, comparing with the two non-condition-specific approaches, the tumor CoRePPI network is found to have the most enriched cancer-related PPIs. Altogether, the results uncover the combined regulatory patterns of TFs and miRNAs on the PPIs, and may provide new insights for research in cancer-associated TFs and miRNAs.
Integrating Global and Local Feature Selection for Multi-Label Learning
Publisher: Association for Computing Machinery (ACM)
Date: 20-02-2023
DOI: 10.1145/3532190
Abstract: Multi-label learning deals with the problem where an instance is associated with multiple labels simultaneously. Multi-label data is often of high dimensionality and has many noisy, irrelevant, and redundant features. As an important machine learning task, multi-label feature selection has received considerable attention in recent years due to its promising performance in dealing with high-dimensional multi-label data. Existing multi-label feature selection methods typically select the global features which are shared by all instances in a dataset. However, these multi-label feature selection methods may be suboptimal since they do not consider the specific characteristics of instances. In this paper, we propose a novel algorithm that integrates Global and Local Feature Selection (GLFS) to exploit both the global features and a subset of discriminative features shared only locally by a subgroup of instances in a multi-label dataset. Specifically, GLFS employs linear regression and ℓ 2,1 -norm on the regression parameters to achieve simultaneous global and local feature selection. Moreover, the proposed algorithm has an effective mechanism for utilizing label correlations to improve the feature selection. Experiments on real-world multi-label datasets show the superiority of GLFS over the state-of-the-art multi-label feature selection methods.
How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?
Publisher: Wiley
Date: 12-04-2022
DOI: 10.1111/BJET.13217
Abstract: With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3‐year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness‐utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context. What is already known about this topic LA is increasingly being used to leverage actionable insights about students and drive student success. LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns. Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models. What this paper adds A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA. Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well. Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled. Implications for practice and/or policy Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness‐minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals. LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives. Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.
Estimating the parameters of mixed Bayesian networks from incomplete data
Publisher: IEEE
Date: 1999
SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection
Publisher: Springer International Publishing
Date: 2018
A pseudotemporal causality approach to identifying miRNA–mRNA interactions during biological processes
Publisher: Oxford University Press (OUP)
Date: 18-10-2021
DOI: 10.1093/BIOINFORMATICS/BTAA899
Abstract: microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA–mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using ‘pseudotime’ concept have inspired us to develop a pseudotime-based method to infer the miRNA–mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process. We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA–mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. R scripts and datasets can be found at github.com/AndresMCB/PTC. Supplementary data are available at Bioinformatics online.
Forensic taxonomy of android productivity apps
Publisher: Springer Science and Business Media LLC
Date: 13-07-2016
Causality-based Feature Selection
Publisher: Association for Computing Machinery (ACM)
Date: 28-09-2020
DOI: 10.1145/3409382
Abstract: Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this article, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at uiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world datasets. Finally, we discuss some challenging problems to be tackled in future research.
Collective behavior learning by differentiating personal preference from peer influence
Publisher: Elsevier BV
Date: 11-2018
Intervention Recommendation for Improving Disability Employment
Publisher: IEEE
Date: 10-12-2020
Effective Outlier Detection based on Bayesian Network and Proximity
Publisher: IEEE
Date: 12-2018
Obtaining the service language for H.245's multimedia capability exchange signalling protocol: the final step
Publisher: IEEE Comput. Soc
LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer
Publisher: Public Library of Science (PLoS)
Date: 23-04-2020
Recommending the Most Effective Intervention to Improve Employment for Job Seekers with Disability
Publisher: ACM
Date: 14-08-2021
Utility Aware Clustering for Publishing Transactional Data
Publisher: Springer International Publishing
Date: 2017
BAMB
Publisher: Association for Computing Machinery (ACM)
Date: 30-09-2019
DOI: 10.1145/3335676
Abstract: The discovery of Markov blanket (MB) for feature selection has attracted much attention in recent years, since the MB of the class attribute is the optimal feature subset for feature selection. However, almost all existing MB discovery algorithms focus on either improving computational efficiency or boosting learning accuracy, instead of both. In this article, we propose a novel MB discovery algorithm for balancing efficiency and accuracy, called underline BA /underline lanced underline M /underline arkov underline B /underline lanket (BAMB) discovery. To achieve this goal, given a class attribute of interest, BAMB finds candidate PC (parents and children) and spouses and removes false positives from the candidate MB set in one go. Specifically, once a feature is successfully added to the current PC set, BAMB finds the spouses with regard to this feature, then uses the updated PC and the spouse set to remove false positives from the current MB set. This makes the PC and spouses of the target as small as possible and thus achieves a trade-off between computational efficiency and learning accuracy. In the experiments, we first compare BAMB with 8 state-of-the-art MB discovery algorithms on 7 benchmark Bayesian networks, then we use 10 real-world datasets and compare BAMB with 12 feature selection algorithms, including 8 state-of-the-art MB discovery algorithms and 4 other well-established feature selection methods. On prediction accuracy, BAMB outperforms 12 feature selection algorithms compared. On computational efficiency, BAMB is close to the IAMB algorithm while it is much faster than the remaining seven MB discovery algorithms.
Mining Markov Blankets Without Causal Sufficiency
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization
Publisher: Oxford University Press (OUP)
Date: 12-06-2017
DOI: 10.1093/BIOINFORMATICS/BTX378
Abstract: Identifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in the Supplementary Material. The package is implemented in R and available under GPL-2 license from the Bioconductor website (ackages/CancerSubtypes/). Supplementary data are available at Bioinformatics online.
A Study of Ten Popular Android Mobile VoIP Applications: Are the Communications Encrypted?
Publisher: IEEE
Date: 2014
Multi-Label Feature Selection Via Adaptive Label Correlation Estimation
Publisher: Association for Computing Machinery (ACM)
Date: 10-08-2023
DOI: 10.1145/3604560
Abstract: In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often have noisy, irrelevant, and redundant features of high dimensionality. Multi-label feature selection has received considerable attention as an effective means for dealing with high-dimensional multi-label data. Many multi-label feature selection methods exploit label correlations to help select features. However, finding label correlations and selecting features in existing multi-label feature selection methods are often two separate processes, the existence of noises and outliers in training data makes the label correlations exploited from label space less reliable. Therefore, the learned label correlations may mislead the feature selection process and result in the selection of less informative features. This article proposes a novel algorithm named ROAD, i.e., multi-label featuRe selectiOn via ADaptive label correlation estimation. ROAD jointly performs adaptive label correlation exploration and feature selection with alternating optimization to obtain reliable estimation of label correlations, which can more effectively reveal the intrinsic manifold structure among labels and lead to the selection of a more proper feature subset. Comprehensive experiments on several frequently used datasets validate the superiority of ROAD against the state-of-the-art multi-label feature selection algorithms.
Causal Decision Trees
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Mining heterogeneous causal effects for personalized cancer treatment
Publisher: Oxford University Press (OUP)
Date: 24-03-2017
DOI: 10.1093/BIOINFORMATICS/BTX174
Abstract: Cancer is not a single disease and involves different subtypes characterized by different sets of molecules. Patients with different subtypes of cancer often react heterogeneously towards the same treatment. Currently, clinical diagnoses rather than molecular profiles are used to determine the most suitable treatment. A molecular level approach will allow a more precise and informed way for making treatment decisions, leading to a better survival chance and less suffering of patients. Although many computational methods have been proposed to identify cancer subtypes at molecular level, to the best of our knowledge none of them are designed to discover subtypes with heterogeneous treatment responses. In this article we propose the Survival Causal Tree (SCT) method. SCT is designed to discover patient subgroups with heterogeneous treatment effects from censored observational data. Results on TCGA breast invasive carcinoma and glioma datasets have shown that for each subtype identified by SCT, the patients treated with radiotherapy exhibit significantly different relapse free survival pattern when compared to patients without the treatment. With the capability to identify cancer subtypes with heterogeneous treatment responses, SCT is useful in helping to choose the most suitable treatment for in idual patients. Data and code are available at github.com/WeijiaZhang24/SurvivalCausalTree. Supplementary data are available at Bioinformatics online.
miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data
Publisher: Informa UK Limited
Date: 06-04-2021
Assessing the Fairness of Course Success Prediction Models in the Face of (Un)equal Demographic Group Distribution
Publisher: ACM
Date: 20-07-2023
Evaluating and Improving SIP Non-INVITE Transaction to Alleviate the Losing Race Problem
Publisher: Springer International Publishing
Date: 2017
miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules
Publisher: Springer Science and Business Media LLC
Date: 10-05-2019
Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs
Publisher: Royal Society of Chemistry (RSC)
Date: 2016
DOI: 10.1039/C5MB00562K
Abstract: We present a causality based framework called mirSRN to infer miRNA synergism in human molecular systems.
Sufficient dimension reduction for average causal effect estimation
Publisher: Springer Science and Business Media LLC
Date: 20-04-2022
DOI: 10.1007/S10618-022-00832-5
Abstract: A large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the number of s les. Propensity score is a common way to deal with a large covariate set, but the accuracy of propensity score estimation (normally done by logistic regression) is also challenged by the large number of covariates. In this paper, we prove that a large covariate set can be reduced to a lower dimensional representation which captures the complete information for adjustment in causal effect estimation. The theoretical result enables effective data-driven algorithms for causal effect estimation. Supported by the result, we develop an algorithm that employs a supervised kernel dimension reduction method to learn a lower dimensional representation from the original covariate space, and then utilises nearest neighbour matching in the reduced covariate space to impute the counterfactual outcomes to avoid the large sized covariate set problem. The proposed algorithm is evaluated on two semisynthetic and three real-world datasets and the results show the effectiveness of the proposed algorithm.
A relative privacy model for effective privacy preservation in transactional data
Publisher: Wiley
Date: 19-09-2018
DOI: 10.1002/CPE.4923
Modelling and Analysis of the INVITE Transaction of the Session Initiation Protocol Using Coloured Petri Nets
Publisher: Springer Berlin Heidelberg
Date: 2008
An Android Communication App Forensic Taxonomy
Publisher: Wiley
Date: 22-07-2016
Abstract: Due to the popularity of Android devices and applications (apps), Android forensics is one of the most studied topics within mobile forensics. Communication apps, such as instant messaging and Voice over IP (VoIP), are one popular app category used by mobile device users, including criminals. Therefore, a taxonomy outlining artifacts of forensic interest involving the use of Android communication apps will facilitate the timely collection and analysis of evidentiary materials from such apps. In this paper, 30 popular Android communication apps were examined, where a logical extraction of the Android phone images was collected using XRY, a widely used mobile forensic tool. Various information of forensic interest, such as contact lists and chronology of messages, was recovered. Based on the findings, a two-dimensional taxonomy of the forensic artifacts of the communication apps is proposed, with the app categories in one dimension and the classes of artifacts in the other dimension. Finally, the artifacts identified in the study of the 30 communication apps are summarized using the taxonomy. It is expected that the proposed taxonomy and the forensic findings in this paper will assist forensic investigations involving Android communication apps.
A novel single-cell based method for breast cancer prognosis
Publisher: Public Library of Science (PLoS)
Date: 24-08-2020
Computational Methods for Predicting Autism Spectrum Disorder from Gene Expression Data
Publisher: Springer International Publishing
Date: 2020
DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
3.2.4 Modelling and Analysis of Internet Multimedia Protocols
Publisher: Wiley
Date: 07-2001
A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling
Publisher: Association for Computing Machinery (ACM)
Date: 04-10-2021
DOI: 10.1145/3466818
Abstract: A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to in iduals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.
A relative privacy model for effective privacy preservation in transactional data
Publisher: IEEE
Date: 08-2017
Practical approaches to causal relationship exploration
Publisher: Springer International Publishing
Date: 2015
Computational methods for cancer driver discovery: A survey
Publisher: Ivyspring International Publisher
Date: 2021
DOI: 10.7150/THNO.52670
Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer
Publisher: Springer Science and Business Media LLC
Date: 08-05-2017
Controller design using coloured Petri nets: with a case study of the papermaking process control
Publisher: IEEE
Date: 06-2011
Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles
Publisher: Public Library of Science (PLoS)
Date: 11-04-2016
Preface to the First IEEE ICDM Workshop on Causal Discovery
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/ICDMW.2013.7
Identifying key factors of student academic performance by subgroup discovery
Publisher: Springer Science and Business Media LLC
Date: 21-06-2018
Causal heterogeneity discovery by bottom-up pattern search for personalised decision making
Publisher: Springer Science and Business Media LLC
Date: 02-08-2023
DOI: 10.1007/S10489-022-03860-2
Abstract: In personalised decision making, evidence is required to determine whether an action (treatment) is suitable for an in idual. Such evidence can be obtained by modelling treatment effect heterogeneity in subgroups. The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an in idual. In this paper, we design a Treatment effect pattern (TEP) to represent treatment effect heterogeneity in data. To achieve an interpretable presentation of TEPs, we use a local causal structure around the outcome to explicitly show how those important variables are used in modelling. We also derive a formula for unbiasedly estimating the Conditional Average Causal Effect (CATE) using the local structure in our problem setting. In the discovery process, we aim at minimising heterogeneity within each subgroup represented by a pattern. We propose a bottom-up search algorithm to discover the most specific patterns fitting in idual circumstances the best for personalised decision making. Experiments show that the proposed method models treatment effect heterogeneity better than three other existing tree based methods in synthetic and real world data sets.
Divide and Conquer: Targeted Adversary Detection using Proximity and Dependency
Publisher: IEEE
Date: 12-2021
Detecting potential signals of adverse drug events from prescription data
Publisher: Elsevier BV
Date: 04-2020
Inferring microRNA and transcription factor regulatory networks in heterogeneous data
Publisher: Springer Science and Business Media LLC
Date: 11-03-2013
Predicting academic performance by considering student heterogeneity
Publisher: Elsevier BV
Date: 12-2018
Building fair predictive models
Publisher: Springer International Publishing
Date: 2020
Inferring and analyzing module-specific lncRNA–mRNA causal regulatory networks in human cancer
Publisher: Oxford University Press (OUP)
Date: 02-2018
DOI: 10.1093/BIB/BBY008
Abstract: It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and erse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA–mRNA interactions and the third-party databases of storing lncRNA–mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA–mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA–mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.
Spectral representation of DNA sequences and its application
Publisher: IEEE
Date: 09-2010
How Many Features Do We Need to Identify Bots on Twitter?
Publisher: Springer Nature Switzerland
Date: 2023
Money laundering and terrorism financing in virtual environments: a feasibility study
Publisher: Emerald
Date: 07-01-2014
DOI: 10.1108/JMLC-06-2013-0019
Abstract: – There is a clear consensus of opinion that virtual environments and virtual currencies pose a money laundering and terrorism financing threat. What is less clear, however, is the level of risk that they pose. This paper aims to clarify the suitability of virtual environments for conducting money laundering and terrorism financing activities. – A number of experiments were conducted to estimate the quantity of funds that could be moved through these environments. These experiments took into account a number of factors such as the number of accounts that would need to be opened to launder/raise a specific amount of funds, the amount of funds that could be placed within a certain timeframe and the transaction limits imposed by each of the massively multiplayer online games and online financial service providers involved in the money laundering and terrorism financing scenarios. – The findings of this research show that money laundering and terrorism financing can take place inside virtual environments. Virtual money laundering and terrorism financing offer high levels of anonymity, potentially low levels of detection, and remove many of the risks associated with real-world money laundering and terrorism financing activity. However, this comes at the cost of ease, time and, in some cases, the amount of funds laundered. Large sums (millions of dollars) can be laundered in virtual environments, but this exponentially increases the level of effort involved in setting up accounts and placing, layering and integrating funds. – A number of authors have described potential virtual money laundering scenarios, but some of these are out-of-date due to closed loopholes, all are rudimentary and make no attempt to discuss the practicality or feasibility of using these scenarios. This research addresses those issues.
An analysis of money laundering and terrorism financing typologies
Publisher: Emerald
Date: 30-12-2011
Estimating heterogeneous treatment effect by balancing heterogeneity and fitness
Publisher: Springer Science and Business Media LLC
Date: 12-2018
Guest editorial: special issue on causal discovery
Publisher: Springer Science and Business Media LLC
Date: 03-2017
Exploring Groups from Heterogeneous Data via Sparse Learning
Publisher: Springer Berlin Heidelberg
Date: 2013
Discovery of Causal Rules Using Partial Association
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/ICDM.2012.36
LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer
Publisher: Oxford University Press (OUP)
Date: 28-06-2018
DOI: 10.1093/BIOINFORMATICS/BTY525
Abstract: MicroRNAs (miRNAs) are small non-coding RNAs with the length of ∼22 nucleotides. miRNAs are involved in many biological processes including cancers. Recent studies show that long non-coding RNAs (lncRNAs) are emerging as miRNA sponges, playing important roles in cancer physiology and development. Despite accumulating appreciation of the importance of lncRNAs, the study of their complex functions is still in its preliminary stage. Based on the hypothesis of competing endogenous RNAs (ceRNAs), several computational methods have been proposed for investigating the competitive relationships between lncRNAs and miRNA target messenger RNAs (mRNAs). However, when the mRNAs are released from the control of miRNAs, it remains largely unknown as to how the sponge lncRNAs influence the expression levels of the endogenous miRNA targets. We propose a novel method to construct lncRNA related miRNA sponge regulatory networks (LncmiRSRNs) by integrating matched lncRNA and mRNA expression profiles with clinical information and putative miRNA-target interactions. Using the method, we have constructed the LncmiRSRNs for four human cancers (glioblastoma multiforme, lung cancer, ovarian cancer and prostate cancer). Based on the networks, we discover that after being released from miRNA control, the target mRNAs are normally up-regulated by the sponge lncRNAs, and only a fraction of sponge lncRNA-mRNA regulatory relationships and hub lncRNAs are shared by the four cancers. Moreover, most sponge lncRNA-mRNA regulatory relationships show a rewired mode between different cancers, and a minority of sponge lncRNA-mRNA regulatory relationships conserved (appearing) in different cancers may act as a common pivot across cancers. Besides, differential and conserved hub lncRNAs may act as potential cancer drivers to influence the cancerous state in cancers. Functional enrichment and survival analysis indicate that the identified differential and conserved LncmiRSRN network modules work as functional units in biological processes, and can distinguish metastasis risks of cancers. Our analysis demonstrates the potential of integrating expression profiles, clinical information and miRNA-target interactions for investigating lncRNA regulatory mechanism. LncmiRSRN is freely available (hangjunpeng411/LncmiRSRN). Supplementary data are available at Bioinformatics online.
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Start Date: 2023
End Date: 12-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP130104090
Start Date: 03-2013
End Date: 11-2016
Amount: $350,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP170101306
Start Date: 06-2017
End Date: 06-2021
Amount: $381,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC170100030
Start Date: 06-2018
End Date: 12-2024
Amount: $4,133,659.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP140103617
Start Date: 06-2014
End Date: 12-2017
Amount: $270,000.00
Funder: Australian Research Council
View Funded Activity