Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in m... more 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. . 2015. From observational studies to causal rule mining.
Motivation: microRNAs (miRNAs) are known to play an essential role in the post-transcriptional ge... more 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. While 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 randomised 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.
Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independ... more 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.
Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide importa... more Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide important insights into the mechanisms of complex human diseases caused by miRNA regulation. Therefore, it is important to identify miRNA synergism and study miRNA characteristics in miRNA synergistic regulatory networks. A number of methods have been proposed to identify miRNA synergism. However, most of the methods only use downstream target genes of miRNAs to infer miRNA synergism when miRNAs can also be regulated by upstream transcription factors (TFs) at the transcriptional level. Additionally, most methods are based on statistical associations identified from data without considering the causal nature of gene regulation. In this paper, we present a causality based framework, called mirSRN (miRNA synergistic regulatory network), to infer miRNA synergism in human molecular systems by considering both downstream miRNA targets and upstream TF regulation. We apply the proposed framework to two real world datasets and discover that almost all the top 10 miRNAs with the largest node degree in the mirSRNs are associated with different human diseases, including cancer, and that the mirSRNs are approximately scale-free and small-world networks. We also find that most miRNAs in the networks are frequently synergistic with other miRNAs, and miRNAs related to the same disease are likely to be synergistic and in a cluster linked to a biological function. Synergistic miRNA pairs show higher co-expression level, and may have potential functional relationships indicating collaboration between the miRNAs. Functional validation of the identified synergistic miRNAs demonstrates that these miRNAs cause different kinds of diseases. These results deepen our understanding of the biological meaning of miRNA synergism.
Background: microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation... more Background: microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships. Results: We propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries. Conclusions: Analysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.
microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miR... more microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g. a cancer dataset from The Cancer Genome Atlas (TCGA), ii) applying the new computational method to the selected dataset, iii) validating the predictions against knowledge from literature and third-party databases, and comparing the performance of the method with some existing methods. This procedure is time consuming given the time elapsed when collecting and processing data, repeating the work from existing methods, searching for knowledge from literature and third-party databases to validate the results, and comparing the results from different methods. The time consuming procedure prevents researchers from quickly testing new computational models, analysing new datasets, and selecting suitable methods for assisting with the experiment design. Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships. The package provides a complete set of pipelines for testing new methods and analysing new datasets. miRLAB includes a pipeline to obtain matched miRNA and mRNA expression datasets directly from TCGA, 12 benchmark computational methods for inferring miRNA-mRNA regulatory relationships, the functions for validating the predictions using experimentally validated miRNA target data and miRNA perturbation data, and the tools for comparing the results from different computational methods.
In recent years, many methods have been developed for detecting causal relationships in observati... more In recent years, many methods have been developed for detecting causal relationships in observational data. Some of them have the potential to tackle large data sets. However, these methods fail to discover a combined cause, i.e. a multi-factor cause consisting of two or more component variables which individually are not causes. A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables. In this paper, we propose a novel approach to address this practical causal discovery problem, i.e. mining combined causes in large data sets. The experiments with both synthetic and real world data sets show that the proposed method can obtain high-quality causal discoveries with a high computational efficiency.
Discovering causal relationships in large databases of observational data is challenging. The pio... more Discovering causal relationships in large databases of observational data is challenging. The pioneering work in this area was rooted in the theory of Bayesian network (BN) learning, which however, is a NP-complete problem. Hence several constraint-based algorithms have been developed to efficiently discover causations in large databases. These methods usually use the idea of BN learning, directly or indirectly, and are focused on causal relationships with single cause variables. In this paper, we propose an approach to mine causal rules in large databases of binary variables. Our method expands the scope of causality discovery to causal relationships with multiple cause variables, and we utilise partial association tests to exclude noncausal associations, to ensure the high reliability of discovered causal rules. Furthermore an efficient algorithm is designed for the tests in large databases. We assess the method with a set of real-world diagnostic data. The results show that our method can effectively discover interesting causal rules in large databases.
Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, in... more Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.
Discovering the regulatory relationships between microRNAs (miRNAs) and mRNAs is an important pro... more 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.
Uncovering causal relationships in data is a major objective of data analytics. Causal relationsh... more Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree where nodes have causal interpretations. Our method follows a well established causal inference framework and makes use of a classic statistical test. The method is practical for finding causal signals in large data sets.
Discovering causal relationships is the ultimate goal of many scientific explorations. Causal rel... more Discovering causal relationships is the ultimate goal of many scientific explorations. Causal relationships can be identified with controlled experiments, but such experiments are often very expensive and sometimes impossible to conduct. On the other hand, the collection of observational data has increased dramatically in recent decades. Therefore it is desirable to find causal relationships from the data directly. Significant progress has been made in the field of discovering causal relationships using the Causal Bayesian Network (CBN) theory. The applications of CBNs, however, are greatly limited due to the high computational complexity. In another direction, association rule mining has been shown to be an efficient data mining means for relationship discovery. However, although causal relationships imply associations, the reverse does not always hold. In this paper we study how to use an efficient association mining approach to discover potential causal rules in observational data. We make use of the idea of retrospective cohort studies, a widely used approach in medical and social research, to detect causal association rules. In comparison with the constraint-based methods within the CBN paradigm, the proposed approach is faster and is capable of finding a cause consisting of combined variables.
microRNAs (miRNAs) are important gene regulators. They control a wide range of biological process... more 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.
Recent findings show that coding genes are not the only targets that miRNAs interact with. In fac... more 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.
Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulato... more Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
Discovering causal relationships from observational data is a crucial problem and has application... more Discovering causal relationships from observational data is a crucial problem and has applications in many research areas. PC algorithm is the state-of-the-art method in the constraint based causal discovery approach. However, the PC algorithm is worst-case exponential to the number of nodes (variables), and thus it is inefficient when applying to high dimensional data, e.g. gene expression datasets where the causal relationships between thousands of nodes (genes) are explored. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelised PC algorithm that is suitable for personal computers and does not require end users' parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we propose a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method on a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from DREAM 5 challenge show that the PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm with 4-core CPU computer managed to finish within around 12.5 hours, and less than 6 hours with a 8-core CPU computer.
Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in m... more 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. . 2015. From observational studies to causal rule mining.
Motivation: microRNAs (miRNAs) are known to play an essential role in the post-transcriptional ge... more 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. While 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 randomised 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.
Recent studies have shown that transcription factors (TFs) and microRNAs (miRNAs), while independ... more 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.
Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide importa... more Understanding the synergism of multiple microRNAs (miRNAs) in gene regulation can provide important insights into the mechanisms of complex human diseases caused by miRNA regulation. Therefore, it is important to identify miRNA synergism and study miRNA characteristics in miRNA synergistic regulatory networks. A number of methods have been proposed to identify miRNA synergism. However, most of the methods only use downstream target genes of miRNAs to infer miRNA synergism when miRNAs can also be regulated by upstream transcription factors (TFs) at the transcriptional level. Additionally, most methods are based on statistical associations identified from data without considering the causal nature of gene regulation. In this paper, we present a causality based framework, called mirSRN (miRNA synergistic regulatory network), to infer miRNA synergism in human molecular systems by considering both downstream miRNA targets and upstream TF regulation. We apply the proposed framework to two real world datasets and discover that almost all the top 10 miRNAs with the largest node degree in the mirSRNs are associated with different human diseases, including cancer, and that the mirSRNs are approximately scale-free and small-world networks. We also find that most miRNAs in the networks are frequently synergistic with other miRNAs, and miRNAs related to the same disease are likely to be synergistic and in a cluster linked to a biological function. Synergistic miRNA pairs show higher co-expression level, and may have potential functional relationships indicating collaboration between the miRNAs. Functional validation of the identified synergistic miRNAs demonstrates that these miRNAs cause different kinds of diseases. These results deepen our understanding of the biological meaning of miRNA synergism.
Background: microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation... more Background: microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships. Results: We propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries. Conclusions: Analysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.
microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miR... more microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g. a cancer dataset from The Cancer Genome Atlas (TCGA), ii) applying the new computational method to the selected dataset, iii) validating the predictions against knowledge from literature and third-party databases, and comparing the performance of the method with some existing methods. This procedure is time consuming given the time elapsed when collecting and processing data, repeating the work from existing methods, searching for knowledge from literature and third-party databases to validate the results, and comparing the results from different methods. The time consuming procedure prevents researchers from quickly testing new computational models, analysing new datasets, and selecting suitable methods for assisting with the experiment design. Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships. The package provides a complete set of pipelines for testing new methods and analysing new datasets. miRLAB includes a pipeline to obtain matched miRNA and mRNA expression datasets directly from TCGA, 12 benchmark computational methods for inferring miRNA-mRNA regulatory relationships, the functions for validating the predictions using experimentally validated miRNA target data and miRNA perturbation data, and the tools for comparing the results from different computational methods.
In recent years, many methods have been developed for detecting causal relationships in observati... more In recent years, many methods have been developed for detecting causal relationships in observational data. Some of them have the potential to tackle large data sets. However, these methods fail to discover a combined cause, i.e. a multi-factor cause consisting of two or more component variables which individually are not causes. A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables. In this paper, we propose a novel approach to address this practical causal discovery problem, i.e. mining combined causes in large data sets. The experiments with both synthetic and real world data sets show that the proposed method can obtain high-quality causal discoveries with a high computational efficiency.
Discovering causal relationships in large databases of observational data is challenging. The pio... more Discovering causal relationships in large databases of observational data is challenging. The pioneering work in this area was rooted in the theory of Bayesian network (BN) learning, which however, is a NP-complete problem. Hence several constraint-based algorithms have been developed to efficiently discover causations in large databases. These methods usually use the idea of BN learning, directly or indirectly, and are focused on causal relationships with single cause variables. In this paper, we propose an approach to mine causal rules in large databases of binary variables. Our method expands the scope of causality discovery to causal relationships with multiple cause variables, and we utilise partial association tests to exclude noncausal associations, to ensure the high reliability of discovered causal rules. Furthermore an efficient algorithm is designed for the tests in large databases. We assess the method with a set of real-world diagnostic data. The results show that our method can effectively discover interesting causal rules in large databases.
Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, in... more Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.
Discovering the regulatory relationships between microRNAs (miRNAs) and mRNAs is an important pro... more 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.
Uncovering causal relationships in data is a major objective of data analytics. Causal relationsh... more Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree where nodes have causal interpretations. Our method follows a well established causal inference framework and makes use of a classic statistical test. The method is practical for finding causal signals in large data sets.
Discovering causal relationships is the ultimate goal of many scientific explorations. Causal rel... more Discovering causal relationships is the ultimate goal of many scientific explorations. Causal relationships can be identified with controlled experiments, but such experiments are often very expensive and sometimes impossible to conduct. On the other hand, the collection of observational data has increased dramatically in recent decades. Therefore it is desirable to find causal relationships from the data directly. Significant progress has been made in the field of discovering causal relationships using the Causal Bayesian Network (CBN) theory. The applications of CBNs, however, are greatly limited due to the high computational complexity. In another direction, association rule mining has been shown to be an efficient data mining means for relationship discovery. However, although causal relationships imply associations, the reverse does not always hold. In this paper we study how to use an efficient association mining approach to discover potential causal rules in observational data. We make use of the idea of retrospective cohort studies, a widely used approach in medical and social research, to detect causal association rules. In comparison with the constraint-based methods within the CBN paradigm, the proposed approach is faster and is capable of finding a cause consisting of combined variables.
microRNAs (miRNAs) are important gene regulators. They control a wide range of biological process... more 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.
Recent findings show that coding genes are not the only targets that miRNAs interact with. In fac... more 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.
Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulato... more Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.
Discovering causal relationships from observational data is a crucial problem and has application... more Discovering causal relationships from observational data is a crucial problem and has applications in many research areas. PC algorithm is the state-of-the-art method in the constraint based causal discovery approach. However, the PC algorithm is worst-case exponential to the number of nodes (variables), and thus it is inefficient when applying to high dimensional data, e.g. gene expression datasets where the causal relationships between thousands of nodes (genes) are explored. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelised PC algorithm that is suitable for personal computers and does not require end users' parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we propose a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method on a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from DREAM 5 challenge show that the PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm with 4-core CPU computer managed to finish within around 12.5 hours, and less than 6 hours with a 8-core CPU computer.
Uploads
Papers by thuc le