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Biological network analysis = trends...

Guzzi, Pietro Hiram, (1980-)

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Biological network analysis = trends, approaches, graph theory, and algorithms /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Biological network analysis/ Pietro Hiram Guzzi, Swarup Roy.
Reminder of title:	trends, approaches, graph theory, and algorithms /
Author:	Guzzi, Pietro Hiram,
other author:	Roy, Swarup
Published:	Cambridge, MA:Elsevier, : 2020.,
Description:	1 online resource (189 p.) :ill.
Subject:	Biomathematics. -
Online resource:	https://www.sciencedirect.com/science/book/9780128193501
ISBN:	9780128193518 (electronic bk.)

Biological network analysis = trends, approaches, graph theory, and algorithms /
Guzzi, Pietro Hiram,1980-

Biological network analysistrends, approaches, graph theory, and algorithms /[electronic resource] :Pietro Hiram Guzzi, Swarup Roy. - Cambridge, MA:Elsevier,2020. - 1 online resource (189 p.) :ill.

Includes bibliographic references and index.

"Complex biological systems and their inter-relationships are represented as graphs or networks. The use of graphs to model biological aspects in computational biology, in bioinformatics and biomedicine is currently growing. Graphs enable researchers to easily model relations among objects. Currently in bioinformatics and systems biology there is a growing interest in the analysis of associations among biological molecules at a network level. A main topic of research in this area is represented by the inference and analysis of biological networks from experimental data. Often researchers aim to analyze differences of evolutions among different networks, i.e., networks representing different states of the same reality or networks coming from different species. Consequently, a wide variety of algorithms have been developed to analyze and compare networks. In particular, the comparison of networks is often performed through network alignmentalgorithms that rely on graph and subgraph isomorphisms. Biological Network Analysis considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The authors discuss various graph-theoretic and data analytics approaches used to analyze these networks with respect to tools, technologies, standards, algorithms, and databases available for generating, representing, and analyzing graphical data." -- Back cover.

ISBN: 9780128193518 (electronic bk.)Subjects--Topical Terms:

523796
Biomathematics.

LC Class. No.: QH323.5

Dewey Class. No.: 570.1/51

Biological network analysis = trends, approaches, graph theory, and algorithms /
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245 1 0 $a Biological network analysis $h [electronic resource] : $b trends, approaches, graph theory, and algorithms / $c Pietro Hiram Guzzi, Swarup Roy.
260 $a Cambridge, MA: $b Elsevier, $c 2020.
300 $a 1 online resource (189 p.) : $b ill.
504 $a Includes bibliographic references and index.
520 $a "Complex biological systems and their inter-relationships are represented as graphs or networks. The use of graphs to model biological aspects in computational biology, in bioinformatics and biomedicine is currently growing. Graphs enable researchers to easily model relations among objects. Currently in bioinformatics and systems biology there is a growing interest in the analysis of associations among biological molecules at a network level. A main topic of research in this area is represented by the inference and analysis of biological networks from experimental data. Often researchers aim to analyze differences of evolutions among different networks, i.e., networks representing different states of the same reality or networks coming from different species. Consequently, a wide variety of algorithms have been developed to analyze and compare networks. In particular, the comparison of networks is often performed through network alignmentalgorithms that rely on graph and subgraph isomorphisms. Biological Network Analysis considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The authors discuss various graph-theoretic and data analytics approaches used to analyze these networks with respect to tools, technologies, standards, algorithms, and databases available for generating, representing, and analyzing graphical data." -- Back cover.
650 0 $a Biomathematics. $3 523796
650 0 $a Graph theory. $3 523815
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650 7 $a Graph theory $v Congresses. $3 1243816
700 1 $a Roy, Swarup $c (Computer scientist), $e author. $3 3508716
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