Clustering Challenges in Biological Networks
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.
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adjacency lists adverse events approach association measures biclustering Bioinformatics biological bipartite graph brain cell classiﬁcation CLUSTER EDITING clustering algorithm clustering analysis coefﬁcient compute concept connectivity consistent biclustering correlation data clustering Data Mining data points data reduction database dataset deﬁned deﬁnition denote density dimensions diversity graph drugs edges efﬁcient EPGOSClust error sum Euclidean distance ﬁnd ﬁrst ﬁxed-parameter function gene expression genetic genome genotypes graph partitioning haplotypes identiﬁcation input interaction networks iteration K-means K-means algorithm kernel lattice Mahalanobis distance matrix maximal maximum clique medoids method microarray motif ﬁnding mRNA mRNA levels neurons NP-hard number of clusters obj(X objects optimal paraclique parameter partitioning PDQ Algorithm problem Proc PROCLUS protein random regulatory samples search tree sequence signiﬁcant signiﬁcantly similarity solution solve speciﬁc structure subgraph subset Theorem tion TL and TR transcription factors values variables vector VERTEX COVER vertices yeast