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ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,”
 BMC Bioinformatics,
, 2006
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Reverse engineering of regulatory networks in human B cells.
 Nat. Genet.
, 2005
"... Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genomewide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new m ..."
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Cited by 178 (2 self)
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Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genomewide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scalefree network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells. Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes controlling common functions, such as the formation of a transcriptional complex or the availability of a signaling pathway. Understanding this organization is crucial to elucidate normal cell physiology as well as to dissect complex pathologic phenotypes. Studies in lower organisms indicate that the structure of both proteinprotein interaction and metabolic networks is of a hierarchical scalefree nature 1,2 , characterized by an inverse relationship between the number of nodes and their connectivity (scalefree) and by a preferential interaction among highly connected genes, called hubs (hierarchical). Although scalefree networks may represent a common blueprint for all cellular constituents, evidence of scalefree topology in higherorder eukaryotic cells is currently limited to coexpression networks 3,4 , which tend to identify entire subpathways rather than individual interactions. Identifying the organizational network of eukaryotic cells is still a key goal in understanding cell physiology and disease. Genomewide clustering of geneexpression profiles has provided an initial step towards the elucidation of cellular networks. But the organization of geneexpression profile data into functionally meaningful genetic information has proven difficult and so far has fallen short of uncovering the intricate structure of cellular interactions. This challenge, called network reverse engineering or deconvolution, has led to an entirely new class of methods aimed at producing highfidelity representations of cellular networks as graphs, where nodes represent genes and edges between them represent interactions, either between the encoded proteins or between the encoded proteins and the genes (we use 'genetic interaction' to refer to both types of mechanisms). Available methods fall into four broad categories: optimization methods 57 , which maximize a scoring function over alternative network models; regression techniques Here we present the successful reverse engineering of geneexpression profile data from human B cells. Our study is based on ARACNe (algorithm for the reconstruction of accurate cellular networks), a new approach for the reverse engineering of cellular networks from microarray expression profiles. ARACNe first identifies statistically significant genegene coregulation by mutual information, an informationtheoretic measure of relatedness. It then eliminates indirect relationships, in which two genes are coregulated through one or more intermediaries, by applying a wellknown staple of data
Bayesian Networks for Genomic Analysis
, 2004
"... Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their applicatio ..."
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Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their application to the analysis of various types of genomic data, from genomic markers to gene expression data. The examples will highlight the potential of this methodology but also the current limitations and we will describe new research directions that hold the promise to make Bayesian networks a fundamental tool for genome data
Reverse engineering and analysis of large genomescale gene networks. Nucleic Acids Res
"... Reverse engineering the wholegenome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast ..."
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Reverse engineering the wholegenome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)based program. The novel features of our approach include: (i) Bsplinebased formulation for lineartime computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce runtime and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the wholegenome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9min on a 1024core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting contextspecific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.
A ProbabilityBased Approach to Soft Discretization for Bayesian Networks
 Georgia Institute of Technology. School of Mechanical Engineering, Tech. Rep
, 2009
"... This report discusses how soft discretization can be implemented to train a discrete Bayesian Network directly from continuous data. The method consists of a soft discretization step that converts the continuous variables of the training cases into soft evidence, followed by a suitable parameter lea ..."
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This report discusses how soft discretization can be implemented to train a discrete Bayesian Network directly from continuous data. The method consists of a soft discretization step that converts the continuous variables of the training cases into soft evidence, followed by a suitable parameter learning algorithm for the Bayesian Network. The learning algorithm is a modification of the Maximum Likelihood Estimation algorithm which is modified to accept soft evidence as input. We also discuss how to use soft discretization for inference and how to convert the inference results from the discrete network to meaningful continuous output values. Most literature on the use of soft discretization for Bayesian Networks proposes to use fuzzy set theory which is based on membership functions. Our approach goes back one step further and starts out with a probability density function that spreads the influence of a continuous variable to its neighbors, followed by a discretization step. Thus our approach to soft discretization is based on probability theory, rather than fuzzy set theory. We then show an interesting connection between these approaches. Namely, a membership function can be generated from the probability density function through convolution, yielding a set of probabilitybased membership functions.
Heuristics for dependency conjectures in proteomic signaling pathways
 In Proceedings of the 43rd Annual Association for Computing Machinery Southeast Conference
, 2005
"... A key issue in the study of protein signaling networks is understanding the relationships among proteins in the network. Understanding these relationships in the context of a network is one of the major challenges for modern biology [2, 6]. In the laboratory a time series of protein modification mea ..."
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A key issue in the study of protein signaling networks is understanding the relationships among proteins in the network. Understanding these relationships in the context of a network is one of the major challenges for modern biology [2, 6]. In the laboratory a time series of protein modification measurements is taken in order that relationships among the activations can be conjectured. Laubenbacher and Stigler [5] have developed an algorithm to make conjectures concerning gene expression. Their algorithm analyses the relations as variables in polynomials, using techniques based in computational algebra. This paper focuses on heuristics for applying their method to conjecture dependencies between proteins in signal transduction networks.
Machine learning and Genetic regulatory networks: A review . . .
"... Genetic regulatory networks are large graphical structures and their inference is a central problem in bioinformatics. However, because of the paucity of the training data and its noisiness, machine learning is essential to good and tractable inference. This literature review first surveys the rele ..."
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Genetic regulatory networks are large graphical structures and their inference is a central problem in bioinformatics. However, because of the paucity of the training data and its noisiness, machine learning is essential to good and tractable inference. This literature review first surveys the relevant theoretical and empirical biochemistry. Next it describes the two types of GRN inference that are problems, the data which can be used for machine learning, and how different kinds of machine learning have been used in previous research. The survey concludes with an analysis of the field as a whole, some underlying methodological issues and a few possible areas for future research.
Multimodal networks in biology
, 2005
"... A multimodal network (MMN) is a novel mathematical construct that captures the structure of biological networks, computational network models, and relationships from biological databases. An MMN subsumes the structure of graphs and hypergraphs, either undirected or directed. Formally, an MMN is a tr ..."
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A multimodal network (MMN) is a novel mathematical construct that captures the structure of biological networks, computational network models, and relationships from biological databases. An MMN subsumes the structure of graphs and hypergraphs, either undirected or directed. Formally, an MMN is a triple (V,E,M) where V is a set of vertices, E is a set of modal hyperedges, and M is a set of modes. A modal hyperedge e = (T,H,A,m) ∈ E is an ordered 4tuple, in which T,H,A ⊆ V and m ∈ M. The sets T, H, and A are the tail, head, and associate of e, while m is its mode. In the context of biology, each vertex is a biological entity, each hyperedge is a relationship, and each mode is a type of relationship (e.g., ‘forms complex ’ and ‘is a’). Within the space of multimodal networks M, structural operations such as union, intersection, hyperedge contraction, subnetwork selection, and graph or hypergraph projections can be performed. A denotational semantics approach is used to specify the semantics of each hyperedge in MMN in terms of interaction among its vertices. This is done by mapping each hyperedge e to a hyperedge code algo V (e), an algorithm that details how the vertices in V (e) get used and updated. A semantic MMNbased model is a function of a given schedule of evaluation of hyperedge codes and the current state of the model, a set of vertexvalue pairs.
ABSTRACT MODELING BIOLOGICAL SYSTEMS FROM HETEROGENEOUS DATA
, 2008
"... Most highthroughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze such heterogeneous data, but also model their complex experimental obser ..."
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Most highthroughput biological data are inherently heterogeneous, providing information at the various levels at which organisms integrate inputs to arrive at an observable phenotype. Approaches are needed to not only analyze such heterogeneous data, but also model their complex experimental observation procedures. We first present a graphical model approach for learning dynamic cell cycle regulatory networks. Our algorithm combines evidence from gene expression data through a likelihood term and proteinDNA binding data through an informative structure prior. We next demonstrate how analysis of cell cycle measurements from a synchronized population of cells are obstructed by synchrony loss. We introduce a probabilistic model, CLOCCS, capable of characterizing multiple sources of asynchrony in synchronized cell populations. Using CLOCCS, we formulate a convex optimization deconvolution procedure that recovers single cell estimates from observed populationlevel measurements. Our algorithm offers a solution for monitoring individual cells rather than a population of cells losing synchrony over time. Using our deconvolution algorithm, we provide a global high
Computational methods for the Inference of Gene Regulatory Networks
"... Summary In the last 10 years, Inference of Gene Regulatory Networks from Microarray data has become an important research area in Bioinformatics. Several algorithms have been proposed and Regulatory networks have been inferred with good accuracy for Saccromycin cervisea, Escherichia coli, Hela cell ..."
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Summary In the last 10 years, Inference of Gene Regulatory Networks from Microarray data has become an important research area in Bioinformatics. Several algorithms have been proposed and Regulatory networks have been inferred with good accuracy for Saccromycin cervisea, Escherichia coli, Hela cells etc. A large amount of knowledge on various biological systems, e.g. gene regulation, metabolic regulations, and signal transduction are being continually accumulated over the years, though there remains a large portion that is not well understood. This paper is a survey of different techniques used for the inference of Gene regulatory networks.