Biological Network Analysis Trends, Approaches, Graph Theory, and Algorithms

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes.

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Bibliographic Details
Main Authors: Guzzi, Pietro Hiram, Roy, Swarup
Format: eBook
Language:English
Published: Chantilly Elsevier Science & Technology 2020
Edition:1
Subjects:
Biomathematics
ISBN:9780128193501, 0128193506
Online Access:Get full text
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Table of Contents:
  • 6.4 Inference assessment designs -- 6.4.1 Assessment against gold standard -- 6.4.2 Assessment in absence of true networks -- 6.4.3 Experimenting with few inference methods -- 6.5 Post inference network analysis -- 6.5.1 Network module detection -- 6.5.2 Ranking key diseased genes using network analysis -- 6.6 Network visualization and analysis tools -- 6.7 Summary -- Acknowledgment -- References -- 7 Protein interaction networks -- 7.1 Proteins and interaction graph -- 7.2 Methods for protein interaction generation -- 7.2.1 Experimental investigation of physical interactions -- 7.2.2 In silico inference of PPI -- 7.3 Fitting protein networks into models -- 7.3.1 Searching the best tting model -- 7.4 PPI network alignment for comparison -- 7.4.1 Pairwise alignments -- 7.4.2 Multiple alignment -- 7.5 Protein networks complex detection -- 7.6 Summary -- Acknowledgment -- References -- 8 Brain connectome networks and analysis -- 8.1 Human brain connectome -- 8.2 From neuroimaging to brain graph -- 8.3 Data storage and querying -- 8.4 Analysis of brain networks: tools and methodologies -- 8.4.1 Topological characterization of connectome networks -- 8.4.2 Comparison of brain networks -- 8.4.3 Analyzing brain network in R: the BrainGraph package -- 8.5 Summary -- Acknowledgment -- References -- 9 Conclusion -- Index -- Back Cover
  • Front Cover -- Biological Network Analysis -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgment -- Abstract -- 1 Introduction -- 1.1 What is a biological network? -- 1.2 Technologies for network data production -- 1.3 Network analysis models -- 1.4 Organization of the book -- References -- 2 Preliminaries of graph theory -- 2.1 Basic concepts -- 2.2 Data structure for representing graphs -- 2.2.1 Array representation -- 2.2.2 List representation -- 2.3 Trees -- 2.3.1 A rooted tree -- 2.3.2 Binary tree -- 2.3.3 An unrooted tree -- 2.3.4 Representation of a tree -- 2.4 Implementing graphs in R -- 2.4.1 More graphs in R -- 2.5 Summary -- References -- 3 Graph analysis -- 3.1 Traversing a graph -- 3.1.1 Breadth rst search (BFS) -- 3.1.2 Depth- rst search (DFS) -- 3.2 Graph traversal at a glance -- 3.3 Shortest paths in a graph -- 3.3.1 Dijkstra's shortest path rst algorithm -- 3.3.2 Handling negative edge weights: the Bellman-Ford algorithm -- 3.3.3 A dynamic programming approach: the Floyd-Warshall algorithm -- 3.4 Power graph analysis -- 3.5 Network centrality measures -- 3.5.1 Degree centrality -- 3.5.2 Closeness centralities -- 3.5.3 Betweenness centralities -- 3.5.4 Eigenvector centralities -- 3.6 Graph community and discovery -- 3.6.1 Few community detection methods -- 3.7 R scripts for graph analysis -- 3.7.1 Graph traversal using R -- 3.7.2 R scripts for community discovery -- 3.7.3 Network centrality analysis in R -- 3.8 Summary -- Acknowledgment -- References -- 4 Complex network models -- 4.1 Complex graphs -- 4.2 Topological characteristics of networks -- 4.2.1 Average path length -- 4.2.2 Clustering coef cient -- 4.2.3 Degree distribution -- 4.2.4 Rich club coef cient -- 4.2.5 Assortativity -- 4.2.6 Modularity -- 4.3 Network models -- 4.3.1 Random networks -- 4.3.2 Small-world networks -- 4.3.3 Scale-free networks
  • 4.3.4 Other models -- 4.3.5 Characteristic analysis of few real networks -- 4.4 Graph modeling in R -- 4.4.1 Modeling complex network -- 4.4.2 Topological analysis -- 4.5 Summary -- Acknowledgment -- References -- 5 Biological network databases -- 5.1 No-SQL and graph databases -- 5.1.1 No-SQL databases in bioinformatics -- 5.1.2 Pros and cons of using No-SQL databases -- 5.2 Genetic interaction network databases -- 5.2.1 RegNetwork (regulatory network repository) -- 5.2.2 TRRUST v2 -- 5.2.3 TRED -- 5.2.4 BioGRID -- 5.2.5 miRTarBase -- 5.2.6 KEGG pathway database -- 5.3 Protein-protein network databases -- 5.3.1 The database of interacting proteins (DIP) -- 5.3.2 Biomolecular interaction network database (BIND) -- 5.3.3 IntAct -- 5.3.4 Online predicted human interaction database (OPHID) -- 5.3.5 Prediction of interactome database (POINT) -- 5.3.6 Integrated network database (IntNetDB) -- 5.3.7 Search tool for the retrieval of interacting genes/proteins (STRING) -- 5.4 Databases of brain networks -- 5.4.1 The healthy brain network (HBN) initiative and its dataset -- 5.4.2 The human connectome project -- 5.4.3 The brain graph project and its database -- 5.5 General purpose repository of networks -- 5.5.1 SNAP: the Stanford network analysis project -- 5.5.2 Network repository -- 5.6 Summary -- References -- 6 Gene expression networks: inference and analysis -- 6.1 Expression network and analysis: the work ow -- 6.2 Basics of gene expression -- 6.2.1 Microarray data generation -- 6.2.2 RNA-seq read counts -- 6.2.3 Single-cell transcriptomics -- 6.2.4 RNA-seq vs. microarray -- 6.2.5 Expression data matrix -- 6.3 Inference of expression networks -- 6.3.1 Inferring causal gene regulatory networks -- 6.3.2 Directed network inference methods -- 6.3.3 Coexpression network inference -- 6.3.4 Undirected network inference methods