Clustering Challenges In Biological Networks

Inbunden, Engelska, 2009

Av AL W ART CHAOVALITWONGSE ET, Al W Art Chaovalitwongse Et, W Art Chaovalitwongse, Sergiy Butenko, Panos M Pardalos, Usa) Chaovalitwongse, W Art (Rutgers Univ, Usa) Butenko, Sergiy (Texas A&m Univ, Usa) Pardalos, Panos M (Univ Of Florida, W. Art Chaovalitwongse

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This volume presents a collection of papers dealing with various aspects of clustering in biological networks and other related problems in computational biology. It consists of two parts, with the first part containing surveys of selected topics and the second part presenting original research contributions. This book will be a valuable source of material to faculty, students, and researchers in mathematical programming, data analysis and data mining, as well as people working in bioinformatics, computer science, engineering, and applied mathematics. In addition, the book can be used as a supplement to any course in data mining or computational/systems biology.

Produktinformation

Utgivningsdatum2009-02-12
Mått157 x 235 x 23 mm
Vikt655 g
FormatInbunden
SpråkEngelska
Antal sidor348
FörlagWorld Scientific Publishing Co Pte Ltd
ISBN9789812771650

Tillhör följande kategorier

Biologi inom Naturvetenskap och teknik
Biokemisk teknik inom Naturvetenskap och teknik

A Novel Clustering Approach: Global Optimum Search with Enhanced Positioning (Tan & Floudas); Mathematical Programming Methods for Comparison Problems in Biocomputing (Oliveira); Classification vs. Clustering: Analyzing Gene Functionality (Perlich); A Projected Clustering Algorithm and Its Biological Application (Deng & Wu); Clique Relaxation Models of Clusters in Biological Networks (Butenko et al.); Analysis of Interaction Networks from Clusters of Co-expressed Genes: A Case Study on Inflammation (Androulakis et al.); Diversity Graphs (Blain et al.); Fixed-Parameter Algorithms for Graph-Modeled Data Clustering (Huffner et al.); Relating Subjective and Objective Pharmacovigilance Association Measures (Pearson); A Novel Similarity-based Modularity Function for Graph Partitioning (Feng et al.); Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data (Eblen et al.); Graph Modeling for Clustering and Motif Findings in Biological Data (Zaslavsky & Sighn); Clustering Approach for Predicting Functions of Unknown mRNA Molecules from Their Dissipative Structures Observed in Glucose-Derepressed Saccharomyces cerevisiae (Sung et al.).