CLUSTERING CHALLENGES IN BIOLOGICAL NETWORKS
edited by W Art Chaovalitwongse (Rutgers University, USA), Sergiy Butenko (Texas A&M University, USA) & Panos M Pardalos (University of Florida, USA)
This text offers introductory knowledge of a wide range of clustering and other quantitative techniques used to solve biological problems. It provides a detailed overview of the practical aspects of real-life biological problems, thus helping researchers identify current and future challenges or trends arising in the research areas concerning clustering problems in biological networks. With contributions from experts in diverse disciplines, readers will learn more about biology from massive data networks through quantitative methods such as clustering and classification. Aimed at senior-level students, investigators, and practitioners from the science, engineering, and medical domains, this book will help them share important concepts, ideas, and scientific methodology in order to boost the knowledge of this relatively new and exciting topic.
Contents:
- 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.)
Readership: Advanced undergraduate and graduate students in engineering,
computer science, mathematics, and biology; researchers and practitioners in biological studies and data mining; nonexperts interested in investigating biological systems.
| 350pp (approx.) |
Pub. date: Scheduled Winter 2008 |
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