Clustering Challenges in Biological NetworksThis 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. |
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adjacency lists adverse events approach association measures biclustering Bioinformatics biological bipartite graph brain cell CLUSTER EDITING clustering algorithm clustering analysis computational concept connectivity consistent biclustering correlation corresponding critical nodes data clustering Data Mining data points data reduction database dataset defined denote density dimensions diversity graph drugs edges error sum fixed-parameter algorithms fixed-parameter tractable function gene expression genetic genome genotypes graph partitioning haplotypes heuristic hubs identify infliximab input interaction networks iteration K-means K-means algorithm kernel lattice Mahalanobis distance Manhattan segmental distance matrix maximal maximum clique medoids method microarray motif finding mRNA mRNA levels neurons NP-hard number of clusters obj(XS objects paraclique parameter partitioning PDQ Algorithm polynomial problem Proc PROCLUS protein random regulatory samples search tree selected sequence similarity solve structure subgraph subset Theorem tion TL and TR transcription factors values variables vector VERTEX COVER vertices yeast