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Statistical and Machine Learning Approaches for Network Analysis by Subhash C. Basak, Matthias Dehmer

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1.1 Introduction

The above quote by Theodor Holm Nelson, the pioneer of information technology, states a deep interconnectedness among the myriad topics of this world. The biological systems are no exceptions, which comprise of a complex web of biomolecular interactions and regulation processes. In particular, the field of computational systems biology aims to arrive at a theory that reveals complicated interaction patterns in the living organisms, which result in various biological phenomenon. Recognition of such patterns can provide insights into the biomolecular activities, which pose several challenges to biology and genetics. However, complexity of biological systems and often an insufficient amount of data used to capture these activities make a reliable inference of the underlying network topology as well as characterization of various patterns underlying these topologies, very difficult. As a result, two problems that have received a considerable amount of attention among researchers are (1) reverse engineering of biological networks from genome-wide measurements and (2) inference of functional units in large biological networks (Fig 1.1).

Figure 1.1 Approaches addressing two fundamental problems in computational systems biology (1) reconstruction of biological networks from two complementary forms of data resources, gene expression data and gene sets and (2) partitioning of large biological networks to extract functional units. Two classes of problems in network partitioning ...

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