Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Molari, Marco (2013) Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data. [Laurea], Università di Bologna, Corso di Studio in Fisica [L-DM270]
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Abstract

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Abstract
Tipologia del documento
Tesi di laurea (Laurea)
Autore della tesi
Molari, Marco
Relatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
network theory, entropy, statistical mechanics, parallel algorithm, c++, programmation, systems biology.
Data di discussione della Tesi
25 Ottobre 2013
URI

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