Inferring Gene Regulatory Networks from Expression Data using Ensemble Methods
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This chapter presents the basic steps that are required to conduct a genome-scale gene regulatory networks (GRN) inference and network-based functional analysis in an R programming environment. The analysis is performed for a large-scale multiple myeloma gene expression data set. It shows the retrieval of gene expression data sets from the NCBI "GeoDB" database, their preprocessing and probe set summarization for gene annotation based on "Entrez" gene identifiers and gene symbols. The first step for the inference of a GRN is the data retrieval and data preprocessing. The chapter uses a publicly available preprocessed multiple myeloma data set available from "GeoDB" with the accession "GSE4581". The chapter gives basic gene expression data processing requirements for the inference and analysis of GRN by the application of the "bc3net" R package. The "bc3net" is a bagging approach of the "c3net" and aggregates an ensemble of "c3net" GRN that are inferred by bootstrapping a gene expression data set.
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This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.
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This paper motivates the use of qualitative probabilistic networks (QPNs) in conjunction with or in lieu of Bayesian Networks (BNs) for reconstructing gene regulatory networks from microarray expression data. QPNs are qualitative abstractions of Bayesian Networks that replace the conditional probability tables associated with BNs by qualitative influences, which use signs to encode how the values of variables change. We demonstrate that the qualitative influences defined by QPNs exhibit a natural mapping to naturally-occurring patterns of connections, termed network motifs, embedded in Gene Regulatory Networks and present a model that maps QPN constructs to such motifs. The contribution of this paper is that of discovering motifs by mapping their time-series experimental data to QPN influences and using the discovered motifs to aid the process of reconstructing the corresponding gene regulatory network via Dynamic Bayesian Networks (DBNs). The general aim is to compile a model that uses qualitative equivalents of Dynamic Bayesian Networks to explore gene expression networks and their regulatory mechanisms. Although this aim remains under development, the results we have obtained shows success for the discovery of regulatory motifs in Saccharomyces Cerevisiae and their effectiveness in improving the results obtained in terms of reconstruction using DBNs.
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In the post-genome era, designing and conducting novel experiments have become increasingly common for modern researchers. However, the major challenge faced by researchers is surprisingly not the complexity in designing new experiments or obtaining the data generated from the experiments, but instead it is the huge amount of data to be processed and analyzed in the quest to produce meaningful information and knowledge. Gene regulatory network (GRN) inference from gene expression data is one of the common examples of such challenge. Over the years, GRN inference has witnessed a number of transitions, and an increasing amount of new computational and statistical-based methods have been applied to automate the procedure. One of the widely used approaches for GRN inference is the dynamic Bayesian network (DBN). In this review paper, we first discuss the evolution of molecular biology research from reductionism to holism. This is followed by a brief insight on various computational and statistical methods used in GRN inference before focusing on reviewing the current development and applications of DBN-based methods. Keywords: Dynamic bayesian network, gene regulatory networks, network inference, time-series gene expression data.
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Inferring gene regulatory networks from gene expression data is an important task in biological studies. In this work, we proposed an optimization model to infer regulatory relations among the functional genes from expression data based on the structural sparsity and/or prior knowledge. Specifically, we achieved the structural sparsity of the network by implementing a linear programming model, which also satisfies the conditions of the existing knowledge. The gene regulatory network is reconstructed by enforcing the sparse linkages with the consistency to the prior knowledge. The effectiveness of the method are demonstrated by several simulated experiments.
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Bayesian networks have become a commonly used tool for inferring structure of gene regulatory networks from gene expression data. In this framework, genes are mapped to nodes of a graph, and Bayesian techniques are used to determine a set of edges that best explain the data, that is, to infer the underlying structure of the network. This chapter begins with an explanation of the mathematical framework of Bayesian networks in the context of reverse engineering of genetic networks. The second part of this review discusses a number of variations upon the basic methodology, including analysis of discrete vs. continuous data or static vs. dynamic Bayesian networks, different methods of exploring the potentially huge search space of network structures, and the use of priors to improve the prediction performance. This review concludes with a discussion of methods for evaluating the performance of network structure inference algorithms.
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