Offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems. This book provides an overview of the practical aspects of real-life biological problems. It is aimed at senior-level students, investigators, and practitioners from the science, engineering, and medical domains.

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.).