Biological networks inference, also referred to as 'reverse engineering', is the scientific process of using (low- and high-throughput) experimental data, statistical and computational techniques to reconstruct how the elements of the biological network (genes, proteins, signaling molecules, cells) interact and operate as a system. We briefly outline inference and reconstruction approaches of some among the most relevant types of biological networks in molecular biology and neuroscience, and some of the most promising methodologies applied in the recent field of network medicine.

Network Inference and Reconstruction in Bioinformatics

2018

Abstract

Biological networks inference, also referred to as 'reverse engineering', is the scientific process of using (low- and high-throughput) experimental data, statistical and computational techniques to reconstruct how the elements of the biological network (genes, proteins, signaling molecules, cells) interact and operate as a system. We briefly outline inference and reconstruction approaches of some among the most relevant types of biological networks in molecular biology and neuroscience, and some of the most promising methodologies applied in the recent field of network medicine.
2018
Istituto Applicazioni del Calcolo ''Mauro Picone''
Inglese
Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M.
Reference Module in Life Sciences
805
813
978-0-12-811432-2
https://www.sciencedirect.com/science/article/pii/B9780128096338202902
Elsevier Inc.
San Diego
STATI UNITI D'AMERICA
Sì, ma tipo non specificato
Computational biology
Gene co-expression networks
Gene regulatory networks
Metabolic networks
Network analysis
Network medicine
Neuroscience
Protein-protein interactions
Signaling networks
Statistical inference
3
02 Contributo in Volume::02.01 Contributo in volume (Capitolo o Saggio)
268
none
Paolo TieriLorenzo FarinaManuela PettiLaura AstolfiPaola PaciFilippo Castiglione
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/355625
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