Information Technology

STATISTICAL AND MACHINE LEARNING APPROACHES FOR NETWORK ANALYSIS

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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 biomolec- ular interactions and regulation processes. In particular, the field of computational systems biology aims to arrive at a theory that reveals complicated interaction pat- terns in the living organisms, which result in various biological phenomenon. Recog- nition of such patterns can provide insights into the biomolecular activities, which pose several challenges to biology and genetics. However, complexity of biologi- cal 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 characteri- zation 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).

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Publisher

: ., 2012

Collation

1-332

Language

English

ISBN/ISSN

978-0-470-19515-4

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NONE

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STATISTICAL AND MACHINE LEARNING APPROACHES FOR NE

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