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New Approaches of Protein Function Prediction from Protein Interaction Networks contains the critical aspects of PPI network based protein function prediction, including semantically assessing the reliability of PPI data, measuring the functional similarity between proteins, dynamically selecting prediction domains, predicting functions, and establishing corresponding prediction frameworks. Functional annotation of proteins is vital to biological and clinical research and other applications due to the important roles proteins play in various biological processes. Although the functions of some proteins have been annotated via biological experiments, there are still many proteins whose functions are yet to be annotated due to the limitations of existing methods and the high cost of experiments. To overcome experimental limitations, this book helps users understand the computational approaches that have been rapidly developed for protein function prediction. Provides innovative approaches and new developments targeting key issues in protein function prediction Presents heuristic ideas for further research in this challenging area
Book Synopsis New Approaches of Protein Function Prediction from Protein Interaction Networks by : Jingyu Hou
Download or read book New Approaches of Protein Function Prediction from Protein Interaction Networks written by Jingyu Hou and published by Academic Press. This book was released on 2017-01-13 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Approaches of Protein Function Prediction from Protein Interaction Networks contains the critical aspects of PPI network based protein function prediction, including semantically assessing the reliability of PPI data, measuring the functional similarity between proteins, dynamically selecting prediction domains, predicting functions, and establishing corresponding prediction frameworks. Functional annotation of proteins is vital to biological and clinical research and other applications due to the important roles proteins play in various biological processes. Although the functions of some proteins have been annotated via biological experiments, there are still many proteins whose functions are yet to be annotated due to the limitations of existing methods and the high cost of experiments. To overcome experimental limitations, this book helps users understand the computational approaches that have been rapidly developed for protein function prediction. Provides innovative approaches and new developments targeting key issues in protein function prediction Presents heuristic ideas for further research in this challenging area
Proteins perform every function in a cell. With the advent of genome sequencing projects for different organisms, large amounts of DNA and protein sequence data is available, whereas their biological function is still unknown in the most of the cases. Predicting protein function is the most challenging problem in post-genomic era. Using sequence homology, phylogenetic profiles, gene expression data, and function of unknown protein can be predicted. Recently, the large interaction networks constructed from high throughput techniques like Yeast2Hybrid experiments are also used in prediction of protein function. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Based on the concept that a protein performs similar function like its neighbor in protein interaction network, a method is proposed to predict protein function using protein-protein interaction data.This analysis should enlighten the path for predicting unannotated protein function hence identifying diseases and inventing methods of it's cureness.
Book Synopsis Protein Function Prediction from Protein Interaction Network by : Sovan Saha
Download or read book Protein Function Prediction from Protein Interaction Network written by Sovan Saha and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins perform every function in a cell. With the advent of genome sequencing projects for different organisms, large amounts of DNA and protein sequence data is available, whereas their biological function is still unknown in the most of the cases. Predicting protein function is the most challenging problem in post-genomic era. Using sequence homology, phylogenetic profiles, gene expression data, and function of unknown protein can be predicted. Recently, the large interaction networks constructed from high throughput techniques like Yeast2Hybrid experiments are also used in prediction of protein function. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Based on the concept that a protein performs similar function like its neighbor in protein interaction network, a method is proposed to predict protein function using protein-protein interaction data.This analysis should enlighten the path for predicting unannotated protein function hence identifying diseases and inventing methods of it's cureness.
The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
Book Synopsis Protein-protein Interactions and Networks by : Anna Panchenko
Download or read book Protein-protein Interactions and Networks written by Anna Panchenko and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The biological interactions of living organisms, and protein-protein interactions in particular, are astonishingly diverse. This comprehensive book provides a broad, thorough and multidisciplinary coverage of its field. It integrates different approaches from bioinformatics, biochemistry, computational analysis and systems biology to offer the reader a comprehensive global view of the diverse data on protein-protein interactions and protein interaction networks.
This volume explores techniques that study interactions between proteins in different species, and combines them with context-specific data, analysis of omics datasets, and assembles individual interactions into higher-order semantic units, i.e., protein complexes and functional modules. The chapters in this book cover computational methods that solve diverse tasks such as the prediction of functional protein-protein interactions; the alignment-based comparison of interaction networks by SANA; using the RaptorX-ComplexContact webserver to predict inter-protein residue-residue contacts; the docking of alternative confirmations of proteins participating in binary interactions and the visually-guided selection of a docking model using COZOID; the detection of novel functional units by KeyPathwayMiner and how PathClass can use such de novo pathways to classify breast cancer subtypes. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary hardware- and software, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Protein-Protein Interaction Networks: Methods and Protocols is a valuable resource for both novice and expert researchers who are interested in learning more about this evolving field.
Book Synopsis Protein-Protein Interaction Networks by : Stefan Canzar
Download or read book Protein-Protein Interaction Networks written by Stefan Canzar and published by Humana. This book was released on 2019-10-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores techniques that study interactions between proteins in different species, and combines them with context-specific data, analysis of omics datasets, and assembles individual interactions into higher-order semantic units, i.e., protein complexes and functional modules. The chapters in this book cover computational methods that solve diverse tasks such as the prediction of functional protein-protein interactions; the alignment-based comparison of interaction networks by SANA; using the RaptorX-ComplexContact webserver to predict inter-protein residue-residue contacts; the docking of alternative confirmations of proteins participating in binary interactions and the visually-guided selection of a docking model using COZOID; the detection of novel functional units by KeyPathwayMiner and how PathClass can use such de novo pathways to classify breast cancer subtypes. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary hardware- and software, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Protein-Protein Interaction Networks: Methods and Protocols is a valuable resource for both novice and expert researchers who are interested in learning more about this evolving field.
Since the completion of sequencing human genome, uncovering the principles of interactions and the functional roles of proteins has been in the spotlight in this post-genomic era. The interactions between proteins provide insights into the underlying mechanisms of biological processes within a cell. The functions of an unknown protein can be postulated on the basis of its interaction evidence with known proteins. The systematic analysis of protein interaction networks has thus become a primary issue in current Bioinformatics research. A wide range of graph theoretic or statistical approaches have attempted to effectively analyze the protein interaction networks. However, they had a limitation in accuracy and efficiency because of the challenges as following. First, the protein-protein interaction data, generated by large-scale high-throughput experiments, are not reliable. Next, the protein interaction networks are typically structured by complex connectivity.^Finally, each protein performs multiple functions in varying environmental conditions. In this dissertation, I explore the quantitative characterization of protein interaction networks based on their unique features such as small-world phenomenon, scale-free distribution and hierarchical modularity. In particular, I focus on accurate, efficient mining of protein interaction networks for the purpose of identifying functional modules and predicting protein functions. A functional module is defined as a maximal set of proteins that participate in the same function. As a pre-process, the network weighting is applied by the integration of functional knowledge from the Gene Ontology database. The semantic similarity and semantic interactivity measures estimate the interaction reliability, which is assigned to the corresponding edge as a weight.^These weighted interaction networks can facilitate the accurate analysis for functional knowledge discovery. I introduce four different approaches for functional module identification and function prediction. First, in the information flow-based approach, I design a novel information flow model that quantifies the propagation of functional information of a protein over the entire complex network. To efficiently implement this model, I propose a dynamic flow simulation algorithm based on random walks. The flow pattern of a protein, generated by this algorithm, indicates its functional impact on the other proteins. Second, the graph restructuring approach retrieves a protein interaction network into a hub-oriented hierarchical structure based on the new definitions of path strength and centrality. This algorithm thus reveals the hierarchically organized functional modules and hubs.^Next, the association pattern-based approach searches the functional association patterns that frequently occur in a protein interaction network. I apply the frequent sub-graph mining algorithm to the labeled graph that is generated by assigning the set of functions of a protein into the node label. Finally, graph reduction is the technique of simplifying the complex connecting pattern of a protein interaction network. Using the reduced graph, the modularization is performed by the iterative procedure of the minimum weighted cut and node accumulation. The generation of protein-protein interaction data is rapidly proceeding, heightening the demand for advances in computational methods to analyze these complex data sets. The approaches presented in this dissertation employ novel, advanced data-mining techniques to discover valuable functional knowledge hidden in the complex protein interaction networks.^This knowledge can be the underlying bases of practical applications in Biomedical Science, e.g., disease diagnosis and drug development. Currently, explosive amounts of heterogeneous biological data are being produced. Developing effective integration methods for incorporating such data is a promising direction for future research.
Book Synopsis Functional Module Identification and Function Prediction from Protein Interaction Networks by : Young-Rae Cho
Download or read book Functional Module Identification and Function Prediction from Protein Interaction Networks written by Young-Rae Cho and published by . This book was released on 2009 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the completion of sequencing human genome, uncovering the principles of interactions and the functional roles of proteins has been in the spotlight in this post-genomic era. The interactions between proteins provide insights into the underlying mechanisms of biological processes within a cell. The functions of an unknown protein can be postulated on the basis of its interaction evidence with known proteins. The systematic analysis of protein interaction networks has thus become a primary issue in current Bioinformatics research. A wide range of graph theoretic or statistical approaches have attempted to effectively analyze the protein interaction networks. However, they had a limitation in accuracy and efficiency because of the challenges as following. First, the protein-protein interaction data, generated by large-scale high-throughput experiments, are not reliable. Next, the protein interaction networks are typically structured by complex connectivity.^Finally, each protein performs multiple functions in varying environmental conditions. In this dissertation, I explore the quantitative characterization of protein interaction networks based on their unique features such as small-world phenomenon, scale-free distribution and hierarchical modularity. In particular, I focus on accurate, efficient mining of protein interaction networks for the purpose of identifying functional modules and predicting protein functions. A functional module is defined as a maximal set of proteins that participate in the same function. As a pre-process, the network weighting is applied by the integration of functional knowledge from the Gene Ontology database. The semantic similarity and semantic interactivity measures estimate the interaction reliability, which is assigned to the corresponding edge as a weight.^These weighted interaction networks can facilitate the accurate analysis for functional knowledge discovery. I introduce four different approaches for functional module identification and function prediction. First, in the information flow-based approach, I design a novel information flow model that quantifies the propagation of functional information of a protein over the entire complex network. To efficiently implement this model, I propose a dynamic flow simulation algorithm based on random walks. The flow pattern of a protein, generated by this algorithm, indicates its functional impact on the other proteins. Second, the graph restructuring approach retrieves a protein interaction network into a hub-oriented hierarchical structure based on the new definitions of path strength and centrality. This algorithm thus reveals the hierarchically organized functional modules and hubs.^Next, the association pattern-based approach searches the functional association patterns that frequently occur in a protein interaction network. I apply the frequent sub-graph mining algorithm to the labeled graph that is generated by assigning the set of functions of a protein into the node label. Finally, graph reduction is the technique of simplifying the complex connecting pattern of a protein interaction network. Using the reduced graph, the modularization is performed by the iterative procedure of the minimum weighted cut and node accumulation. The generation of protein-protein interaction data is rapidly proceeding, heightening the demand for advances in computational methods to analyze these complex data sets. The approaches presented in this dissertation employ novel, advanced data-mining techniques to discover valuable functional knowledge hidden in the complex protein interaction networks.^This knowledge can be the underlying bases of practical applications in Biomedical Science, e.g., disease diagnosis and drug development. Currently, explosive amounts of heterogeneous biological data are being produced. Developing effective integration methods for incorporating such data is a promising direction for future research.
Proteins are indispensable players in virtually all biological events. The functions of proteins are coordinated through intricate regulatory networks of transient protein-protein interactions (PPIs). To predict and/or study PPIs, a wide variety of techniques have been developed over the last several decades. Many in vitro and in vivo assays have been implemented to explore the mechanism of these ubiquitous interactions. However, despite significant advances in these experimental approaches, many limitations exist such as false-positives/false-negatives, difficulty in obtaining crystal structures of proteins, challenges in the detection of transient PPI, among others. To overcome these limitations, many computational approaches have been developed which are becoming increasingly widely used to facilitate the investigation of PPIs. This book has gathered an ensemble of experts in the field, in 22 chapters, which have been broadly categorized into Computational Approaches, Experimental Approaches, and Others.
Book Synopsis Protein-Protein Interactions by : Weibo Cai
Download or read book Protein-Protein Interactions written by Weibo Cai and published by BoD – Books on Demand. This book was released on 2012-03-30 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteins are indispensable players in virtually all biological events. The functions of proteins are coordinated through intricate regulatory networks of transient protein-protein interactions (PPIs). To predict and/or study PPIs, a wide variety of techniques have been developed over the last several decades. Many in vitro and in vivo assays have been implemented to explore the mechanism of these ubiquitous interactions. However, despite significant advances in these experimental approaches, many limitations exist such as false-positives/false-negatives, difficulty in obtaining crystal structures of proteins, challenges in the detection of transient PPI, among others. To overcome these limitations, many computational approaches have been developed which are becoming increasingly widely used to facilitate the investigation of PPIs. This book has gathered an ensemble of experts in the field, in 22 chapters, which have been broadly categorized into Computational Approaches, Experimental Approaches, and Others.
Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred
Book Synopsis Protein Function Prediction for Omics Era by : Daisuke Kihara
Download or read book Protein Function Prediction for Omics Era written by Daisuke Kihara and published by Springer Science & Business Media. This book was released on 2011-04-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred
This thesis describes the development of PINALOG, a protein interaction network alignment method, and its application to the area of protein function prediction and protein complex detection. Protein-protein interactions (PPI) play an important role in the function of biological processes. Advances in high-throughput technology have produced a large amount of protein-protein interaction data, enabling analyses at the system level. Although protein-protein interaction networks (PPINs) vary between species, there are components of them that perform similar biological functions and these are likely to be conserved across species. Comparison of the protein interaction networks from different species yields understanding of the evolution of species, as well as a means to predict protein function and conserved components. An alignment method, PINALOG, has been developed which globally aligns the similar parts of the networks using information from protein sequences, protein functions and network topology in a seed-and-extend framework. The results on human and yeast network alignment revealed conserved subnetworks that are components of similar biological processes such as the proteasome or transcription related processes. The alignments of several pairs of species confirm the superior performance of PINALOG over commonly used methods such as Graemlin and IsoRank in terms of finding a large conserved network as well as detecting biologically meaningful mappings of the proteins in the two aligned species. The alignment method also suggested an approach to perform protein complex prediction by knowledge transfer from one species to another. In addition the implications for function prediction of proteins in the "twilight" zone where there is little or no sequence similarity were explored. A web server for PINALOG was developed to provide users access to the alignment method.
Book Synopsis Development and Application of a Computational Approach to Align Protein Interaction Networks by : Phan Thi Thu Hang
Download or read book Development and Application of a Computational Approach to Align Protein Interaction Networks written by Phan Thi Thu Hang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes the development of PINALOG, a protein interaction network alignment method, and its application to the area of protein function prediction and protein complex detection. Protein-protein interactions (PPI) play an important role in the function of biological processes. Advances in high-throughput technology have produced a large amount of protein-protein interaction data, enabling analyses at the system level. Although protein-protein interaction networks (PPINs) vary between species, there are components of them that perform similar biological functions and these are likely to be conserved across species. Comparison of the protein interaction networks from different species yields understanding of the evolution of species, as well as a means to predict protein function and conserved components. An alignment method, PINALOG, has been developed which globally aligns the similar parts of the networks using information from protein sequences, protein functions and network topology in a seed-and-extend framework. The results on human and yeast network alignment revealed conserved subnetworks that are components of similar biological processes such as the proteasome or transcription related processes. The alignments of several pairs of species confirm the superior performance of PINALOG over commonly used methods such as Graemlin and IsoRank in terms of finding a large conserved network as well as detecting biologically meaningful mappings of the proteins in the two aligned species. The alignment method also suggested an approach to perform protein complex prediction by knowledge transfer from one species to another. In addition the implications for function prediction of proteins in the "twilight" zone where there is little or no sequence similarity were explored. A web server for PINALOG was developed to provide users access to the alignment method.
The growing flood of new experimental data generated by genome sequencing has provided an impetus for the development of automated methods for predicting the functions of proteins that have been deduced by sequence analysis and lack experimental characterization. Prediction of Protein Structures, Functions and Interactions presents a comprehensive overview of methods for prediction of protein structure or function, with the emphasis on their availability and possibilities for their combined use. Methods of modeling of individual proteins, prediction of their interactions, and docking of complexes are put in the context of predicting gene ontology (biological process, molecular function, and cellular component) and discussed in the light of their contribution to the emerging field of systems biology. Topics covered include: first steps of protein sequence analysis and structure prediction automated prediction of protein function from sequence template-based prediction of three-dimensional protein structures: fold-recognition and comparative modelling template-free prediction of three-dimensional protein structures quality assessment of protein models prediction of molecular interactions: from small ligands to large protein complexes macromolecular docking integrating prediction of structure, function, and interactions Prediction of Protein Structures, Functions and Interactions focuses on the methods that have performed well in CASPs, and which are constantly developed and maintained, and are freely available to academic researchers either as web servers or programs for local installation. It is an essential guide to the newest, best methods for prediction of protein structure and functions, for researchers and advanced students working in structural bioinformatics, protein chemistry, structural biology and drug discovery.
Book Synopsis Prediction of Protein Structures, Functions, and Interactions by : Janusz M. Bujnicki
Download or read book Prediction of Protein Structures, Functions, and Interactions written by Janusz M. Bujnicki and published by John Wiley & Sons. This book was released on 2008-12-23 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growing flood of new experimental data generated by genome sequencing has provided an impetus for the development of automated methods for predicting the functions of proteins that have been deduced by sequence analysis and lack experimental characterization. Prediction of Protein Structures, Functions and Interactions presents a comprehensive overview of methods for prediction of protein structure or function, with the emphasis on their availability and possibilities for their combined use. Methods of modeling of individual proteins, prediction of their interactions, and docking of complexes are put in the context of predicting gene ontology (biological process, molecular function, and cellular component) and discussed in the light of their contribution to the emerging field of systems biology. Topics covered include: first steps of protein sequence analysis and structure prediction automated prediction of protein function from sequence template-based prediction of three-dimensional protein structures: fold-recognition and comparative modelling template-free prediction of three-dimensional protein structures quality assessment of protein models prediction of molecular interactions: from small ligands to large protein complexes macromolecular docking integrating prediction of structure, function, and interactions Prediction of Protein Structures, Functions and Interactions focuses on the methods that have performed well in CASPs, and which are constantly developed and maintained, and are freely available to academic researchers either as web servers or programs for local installation. It is an essential guide to the newest, best methods for prediction of protein structure and functions, for researchers and advanced students working in structural bioinformatics, protein chemistry, structural biology and drug discovery.
Abstract: One of the defining challenges in the post-genomic era is to develop computational and experimental tools to elucidate the mechanisms of bio-molecular interactions within the cell and between the cell and environment. The recent availability of an increasing number of completely sequenced genomes from diverse species has opened new possibilities for systematically annotating large numbers of genes by comparative genomics and deciphering the web of molecular interactions that underlie most cellular systems. High-throughput algorithms that explore the genomic context of genes and capture evolutionary signatures are needed to effectively complement and extend experimental techniques to enhance our knowledge of protein functions at various organizational levels. This thesis explores phylogenetically based computational techniques that systematically analyze large numbers of genomes to infer protein interaction networks and to quantitatively assign uncharacterized proteins to functional classes. We first pursue a statistical approach to identify protein networks using phylogenetic profiles. Next, we develop a mathematical method to determine the pair-wise correlation in the network and quantitatively assign putative functions to previously unknown genes. In addition to the pair-wise functional linkage analysis, we then develop a framework for extracting higher order information in protein interaction networks. Identified statistically significant protein groups not only enrich the functional annotation that is not possible to obtain in the pair-wise case, but also serve as candidates for logical analysis to further decipher the higher order organization of the cell. Finally we analyze the modular components in protein interaction networks that constitute the cell using our online analysis and visualization tool VisAnt . All the inferences drawn from the methods described herein are available online.
Book Synopsis Phylogenetic Approaches to Protein Function Prediction and Protein Network Analysis by : Jie Wu
Download or read book Phylogenetic Approaches to Protein Function Prediction and Protein Network Analysis written by Jie Wu and published by . This book was released on 2006 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: One of the defining challenges in the post-genomic era is to develop computational and experimental tools to elucidate the mechanisms of bio-molecular interactions within the cell and between the cell and environment. The recent availability of an increasing number of completely sequenced genomes from diverse species has opened new possibilities for systematically annotating large numbers of genes by comparative genomics and deciphering the web of molecular interactions that underlie most cellular systems. High-throughput algorithms that explore the genomic context of genes and capture evolutionary signatures are needed to effectively complement and extend experimental techniques to enhance our knowledge of protein functions at various organizational levels. This thesis explores phylogenetically based computational techniques that systematically analyze large numbers of genomes to infer protein interaction networks and to quantitatively assign uncharacterized proteins to functional classes. We first pursue a statistical approach to identify protein networks using phylogenetic profiles. Next, we develop a mathematical method to determine the pair-wise correlation in the network and quantitatively assign putative functions to previously unknown genes. In addition to the pair-wise functional linkage analysis, we then develop a framework for extracting higher order information in protein interaction networks. Identified statistically significant protein groups not only enrich the functional annotation that is not possible to obtain in the pair-wise case, but also serve as candidates for logical analysis to further decipher the higher order organization of the cell. Finally we analyze the modular components in protein interaction networks that constitute the cell using our online analysis and visualization tool VisAnt . All the inferences drawn from the methods described herein are available online.
