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New Approaches Of Protein Function Prediction From Protein Interaction Networks

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New Approaches of Protein Function Prediction from Protein Interaction Networks

New Approaches of Protein Function Prediction from Protein Interaction Networks Book
Author : Jingyu Hou
Publisher : Academic Press
Release : 2017-01-13
ISBN : 0128099445
Language : En, Es, Fr & De

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Book Description :

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

Functional Module Identification and Function Prediction from Protein Interaction Networks

Functional Module Identification and Function Prediction from Protein Interaction Networks Book
Author : Young-Rae Cho
Publisher : Unknown
Release : 2009
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Protein Function Prediction from Protein Interaction Network

Protein Function Prediction from Protein Interaction Network Book
Author : Sovan Saha,Piyali Chatterjee
Publisher : LAP Lambert Academic Publishing
Release : 2013
ISBN : 9783659402784
Language : En, Es, Fr & De

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Book Description :

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.

Phylogenetic Approaches to Protein Function Prediction and Protein Network Analysis

Phylogenetic Approaches to Protein Function Prediction and Protein Network Analysis Book
Author : Jie Wu
Publisher : Unknown
Release : 2006
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Biomolecular Networks

Biomolecular Networks Book
Author : Luonan Chen,Rui-Sheng Wang,Xiang-Sun Zhang
Publisher : John Wiley & Sons
Release : 2009-06-29
ISBN : 9780470488058
Language : En, Es, Fr & De

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Book Description :

Alternative techniques and tools for analyzing biomolecular networks With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach. Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods: GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics.

Development and Application of a Computational Approach to Align Protein Interaction Networks

Development and Application of a Computational Approach to Align Protein Interaction Networks Book
Author : Phan Thi Thu Hang,Michael Sternberg
Publisher : Unknown
Release : 2012
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Protein Function Prediction for Omics Era

Protein Function Prediction for Omics Era Book
Author : Daisuke Kihara
Publisher : Springer Science & Business Media
Release : 2011-04-19
ISBN : 9789400708815
Language : En, Es, Fr & De

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Book Description :

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

Protein protein Interactions and Networks

Protein protein Interactions and Networks Book
Author : Anna Panchenko,Teresa M. Przytycka
Publisher : Springer Science & Business Media
Release : 2010-04-06
ISBN : 9781848001251
Language : En, Es, Fr & De

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Book Description :

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.

Protein Interaction Networks in Health and Disease

Protein Interaction Networks in Health and Disease Book
Author : Spyros Petrakis,Miguel A. Andrade-Navarro
Publisher : Frontiers Media SA
Release : 2016-10-19
ISBN : 2889199827
Language : En, Es, Fr & De

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Book Description :

The identification and mapping of protein-protein interactions (PPIs) is a major goal in systems biology. Experimental data are currently produced in large scale using a variety of high-throughput assays in yeast or mammalian systems. Analysis of these data using computational tools leads to the construction of large protein interaction networks, which help researchers identify novel protein functions. However, our current view of protein interaction networks is still limited and there is an active field of research trying to further develop this concept to include important processes: the topology of interactions and their changes in real time, the effects of competition for binding to the same protein region, PPI variation due to alternative splicing or post-translational modifications, etc. In particular, a clinically relevant topic for development of the concept of protein interactions networks is the consideration of mutant isoforms, which may be responsible for a pathological condition. Mutations in proteins may result in loss of normal interactions and appearance of novel abnormal interactions that may affect a protein’s function and biological cycle. This Research Topic presents novel findings and recent achievements in the field of protein interaction networks with a focus on disease. Authors describe methods for the identification and quantification of PPIs, the annotation and analysis of networks, considering PPIs and protein complexes formed by mutant proteins associated with pathological conditions or genetic diseases.

Protein Interaction Networks

Protein Interaction Networks Book
Author : Aidong Zhang
Publisher : Cambridge University Press
Release : 2009-04-06
ISBN : 0521888956
Language : En, Es, Fr & De

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Book Description :

The first full survey of statistical, topological, data-mining, and ontology-based methods for analyzing protein-protein interaction networks.

Network based Information Integration for Protein Function Prediction

Network based Information Integration for Protein Function Prediction Book
Author : Xiaoyu Jiang
Publisher : Unknown
Release : 2009
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Abstract: Protein function prediction is a fundamental problem in computational biology. For protein activities described by terms in databases such as the Gene Ontology (GO), this task is typically pursued as a binary classification problem. As a result of an astonishing increase in the available genome-wide protein information, integrating different protein datasets has become a significant opportunity and a major focus to infer functionality. This dissertation contains three novel approaches to integrate popular protein information to classify proteins into functional categories. A probabilistic method, Hierarchical Binomial-Neighborhood (HBN), combining proteins' relational information from the protein-protein interaction (PPI) network, together with the GO hierarchical structure, is proposed first. Results from comparing analogous models on terms from the biological process ontology and genes from the yeast genome show substantial improvement and further analysis illustrates that such an improvement is uniformly consistent with the GO depth. Being aware of the fact that the gene interaction knowledge is still incomplete in most organisms, the second approach we develop is an aggressively integrative probabilistic framework, Probabilistic Hierarchical Inferences for Protein Activity (PHIPA), with improved data usage efficiency, for combining protein relational network, categorical motif and cellular localization information and the GO hierarchy. We implement it on a network extracted from an integrative protein-protein association databases STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Being based on Nearest-Neighbor, or the "guilt-by-association" counting principle, both HBN and PHIPA use only the local neighborhood information, and are therefore built on local probabilistic models. In contrast, we develop a third approach, a fully Bayesian network-based auto-probit framework encoding the functional similarity influenced by the network topology. We not only show that the auto-probit model works equally well in prediction as the "local" methods, but also demonstrate its capability of producing more potentially interesting protein predictions by taking advantage of GO annotation uncertainty, which is critical in using and improving the GO database but yet has been ignored by most existing methodologies in this context.

Structure driven Approaches to Protein protein Recognition

Structure driven Approaches to Protein protein Recognition Book
Author : Julian Mintseris
Publisher : Unknown
Release : 2006
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Abstract: Much of our understanding of protein function arises from the cellular context in which the protein operates. While two proteins may be functionally linked in a variety of ways, the most direct way for them to interact is through physical recognition of the protein surface followed by a binding event. If the function of a single protein can be understood in terms of its interactions, then the function of a biological system as a whole can be viewed through the network of protein interactions. I use structure-driven approaches to gain additional insight into the organization of protein interaction networks by showing distinct differences between transient and obligate protein interactions. This important distinction can be detected on a purely structural level by comparing the pair-wise contact frequencies between different types of atoms at the protein complex interface. On the functional level, the distinction can be made by looking at the curated ontology annotations. Proteins involved in transient and obligate interactions have been subject to different levels of evolutionary pressure and traces of these differences can be detected by considering their evolutionary histories. Residues in the interfaces of obligate complexes tend to evolve at a relatively slower rate, allowing them to co-evolve with their interacting partners. In contrast, the plasticity inherent in transient interactions leads to an increased rate of substitution for the interface residues and leaves little or no evidence of correlated mutations. Recent advances in high-throughput proteomic technologies combined with computational approaches have identified large numbers of putative novel interactions. However both experimental and computational approaches tend to do better identifying components of large obligate complexes, while fleeting interactions crucial in systems such as signaling cascades and immune response are harder to predict. To this end, I developed new representations of protein structure and derived empirical potentials for protein-protein docking, improving on our ability to predict the complex structures of transient complexes from individually crystallized components.

Data Management of Protein Interaction Networks

Data Management of Protein Interaction Networks Book
Author : Mario Cannataro,Pietro H. Guzzi
Publisher : John Wiley & Sons
Release : 2012-02-03
ISBN : 1118103734
Language : En, Es, Fr & De

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Book Description :

Current PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e.g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed.

Probabilistic Integration of Heterogeneous Contextual and Cross species Genome wide Data for Protein Function Prediction

Probabilistic Integration of Heterogeneous  Contextual  and Cross species Genome wide Data for Protein Function Prediction Book
Author : Naoki Nariai
Publisher : Unknown
Release : 2010
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Abstract: Completed genome sequences from many organisms have revealed many genes with no known function. A critical challenge is the development of methods that will aid in the discovery of the molecular functions of the newly discovered genes, while identifying the biological processes in which these genes participate. Current sequence-based methods frequently fail to annotate gene function accurately. New computational approaches combining genomic, transcriptional and proteomic data generated from high-throughput technologies offer potential routes toward predictions of increased accuracy and greater coverage of unknowns. In this thesis, we describe and evaluate several probabilistic methods for protein function prediction that integrate heterogeneous genome-wide data, such as protein-protein interaction (PPI) data, mRNA expression data, protein domain, and localization information under a Bayesian framework. In a cross validation study in yeast, with the goal of predicting the Gene Ontology "biological process" terms, our integrated method increases recall by 18% over methods that only use PPI data, at 50% precision. We compared prediction accuracies in five different model organisms (human, mouse, fly, worm and yeast). Of the various types of genome-wide data incorporated, we found that PPI data contributes most significantly to the improved precision of predictions in yeast. We also develop a context-specific approach for protein function prediction in order to capture dependencies among the various types of biological information listed above. We found that context-specific methods improve prediction precision in some cases, but can also degrade performance for some predictions. Finally, we developed a method to integrate PPI networks between different species through homology mapping. We predict genes that participate in the insulin signaling pathway. This pathway is highly conserved between human and worm, and of profound biological and medical interest given its roles in diabetes and aging. In a cross validation study, our method which derives PPI relationships from both organisms significantly improved prediction performance over a method that only uses PPI data from either human or worm. We produce a large number of predictions in which a number of cases have reasonable literature support.

Protein protein Interactions and Networks

Protein protein Interactions and Networks Book
Author : Anna Panchenko,Teresa M. Przytycka
Publisher : Springer
Release : 2010-10-22
ISBN : 9781849967310
Language : En, Es, Fr & De

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Book Description :

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.

Prediction of Protein Structures Functions and Interactions

Prediction of Protein Structures  Functions  and Interactions Book
Author : Janusz M. Bujnicki
Publisher : John Wiley & Sons
Release : 2008-12-23
ISBN : 9780470741900
Language : En, Es, Fr & De

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Book Description :

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.

Computational Prediction of Protein Complexes from Protein Interaction Networks

Computational Prediction of Protein Complexes from Protein Interaction Networks Book
Author : Sriganesh Srihari,Chern Han Yong,Limsoon Wong
Publisher : Morgan & Claypool
Release : 2017-05-30
ISBN : 1970001534
Language : En, Es, Fr & De

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Book Description :

Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.

Genome Wide Prediction and Analysis of Protein Protein Functional Linkages in Bacteria

Genome Wide Prediction and Analysis of Protein Protein Functional Linkages in Bacteria Book
Author : Vijaykumar Yogesh Muley,Vishal Acharya
Publisher : Springer Science & Business Media
Release : 2012-07-28
ISBN : 1461447054
Language : En, Es, Fr & De

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Book Description :

​​ ​Using genome sequencing, one can predict possible interactions among proteins. There are very few titles that focus on protein-protein interaction predictions in bacteria. The authors will describe these methods and further highlight its use to predict various biological pathways and complexity of the cellular response to various environmental conditions. Topics include analysis of complex genome-scale protein-protein interaction networks, effects of reference genome selection on prediction accuracy, and genome sequence templates to predict protein function.

Research in Computational Molecular Biology

Research in Computational Molecular Biology Book
Author : Benny Chor
Publisher : Springer
Release : 2012-04-13
ISBN : 3642296270
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012, held in Barcelona, Spain, in April 2012. The 31 revised full papers presented together with 5 keynote lectures were carefully reviewed and selected from 200 submissions. The papers feature current research in all areas of computational molecular biology, including: molecular sequence analysis; recognition of genes and regulatory elements; molecular evolution; protein structure; structural genomics; analysis of gene expression; biological networks; sequencing and genotyping technologies; drug design; probabilistic and combinatorial algorithms; systems biology; computational proteomics; structural and functional genomics; information systems for computational biology and imaging.

Advances in Biotechnology Research and Application 2012 Edition

Advances in Biotechnology Research and Application  2012 Edition Book
Author : Anonim
Publisher : ScholarlyEditions
Release : 2012-12-26
ISBN : 1464990832
Language : En, Es, Fr & De

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Book Description :

Advances in Biotechnology Research and Application / 2012 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Biotechnology. The editors have built Advances in Biotechnology Research and Application / 2012 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Biotechnology in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Biotechnology Research and Application / 2012 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.