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Sergiy Butenko

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.
Contents: Surveys of Selected Topics: Fixed-Parameter Algorithms for Graph-Modeled Data Clustering (HÃ¼ffner et al.); Probabilistic Distance Clustering: Algorithm and Applications (C Iyigun & A Ben-Israel); Analysis of Regulatory and Interaction Networks from Clusters of Co-expressed Genes (E Yang et al.); Graph-based Approaches for Motif Discovery (E Zaslavsky); Statistical Clustering Analysis: An Introduction (H Zhang); New Methods and Applications: Diversity Graphs (P Blain et al.); Identifying Critical Nodes in Protein-Protein Interaction Networks (V Boginski & C W Commander); Faster Algorithms for Constructing a Concept (Galois) Lattice (V Choi); A Projected Clustering Algorithm and Its Biomedical Application (P Deng & W Wu); Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data (J D Eblen et al.); A Novel Similarity-based Modularity Function for Graph Partitioning (Z Feng et al.); Mechanism-based Clustering of Genome-wide RNA Levels: Roles of Transcription and Transcript-Degradation Rates (S Ji et al.); The Complexity of Feature Selection for Consistent Biclustering (O E Kundakcioglu & P M Pardalos); Clustering Electroencephalogram Recordings to Study Mesial Temporal Lobe Epilepsy (C-C Liu et al.); Relating Subjective and Objective Pharmacovigilance Association Measures (R K Pearson); A Novel Clustering Approach: Global Optimum Search with Enhanced Positioning (M P Tan & C A Floudas).

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