Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1 Dense Module Enumeration in Biological Networks . . . . . . . . . . . . . . . . . . . . . . . . . 1
Koji Tsuda and Elisabeth Georgii
2 Discovering Interacting Domains and Motifs
in Protein–Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Willy Hugo, Wing-Kin Sung, and See-Kiong Ng
3 Global Alignment of Protein–Protein Interaction Networks . . . . . . . . . . . . . . . . . . . 21
Misael Mongiovı` and Roded Sharan
4 Structure Learning for Bayesian Networks as Models of Biological Networks . . . . 35
Antti Larjo, Ilya Shmulevich, and Harri L



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