Lives on earth are regulated by a complex system of interactions. Modelling these interactions through the network paradigms allows researchers to discover and understand the fundamental molecular mechanisms which drive the biological processes and lead to human diseases. The advancement made in the development of sequencing technologies has produced a growing amount of biological data. The aforementioned preconditions are at the base of a flourishing production of deep learning methods able to cope with the complexity and data abundance of this domain. For these reasons, this chapter provides a comprehensive overview of the recent advancements in the deep learning network-based approaches focusing on biology, medicine and pharmacological crucial research problems. At first, the needed biological and network science backgrounds are presented. Second, a comprehensive overview of the biological networks and resources is provided. Finally, we discuss the most recent methods in the field, organising them into three broad categories: the interactome, the network pharmacology, and the frontier biological problems.

Deep learning methods for network biology / Madeddu, Lorenzo; Stilo, Giovanni. - (2022), pp. 197-246. [10.1142/9781800610941_0007].

Deep learning methods for network biology

Madeddu, Lorenzo;Stilo, Giovanni
2022

Abstract

Lives on earth are regulated by a complex system of interactions. Modelling these interactions through the network paradigms allows researchers to discover and understand the fundamental molecular mechanisms which drive the biological processes and lead to human diseases. The advancement made in the development of sequencing technologies has produced a growing amount of biological data. The aforementioned preconditions are at the base of a flourishing production of deep learning methods able to cope with the complexity and data abundance of this domain. For these reasons, this chapter provides a comprehensive overview of the recent advancements in the deep learning network-based approaches focusing on biology, medicine and pharmacological crucial research problems. At first, the needed biological and network science backgrounds are presented. Second, a comprehensive overview of the biological networks and resources is provided. Finally, we discuss the most recent methods in the field, organising them into three broad categories: the interactome, the network pharmacology, and the frontier biological problems.
2022
Deep Learning in Biology and Medicine
978-1-80061-093-4
978-1-80061-094-1
Machine learning; deep learning; graph mining; bioinformatics; computational biology; biology; medicine; network biology; network medicine; network pharmacology
02 Pubblicazione su volume::02a Capitolo o Articolo
Deep learning methods for network biology / Madeddu, Lorenzo; Stilo, Giovanni. - (2022), pp. 197-246. [10.1142/9781800610941_0007].
File allegati a questo prodotto
File Dimensione Formato  
Madeddu__Deep-learning-in-Biology-and-Medicine_2022.pdf

solo gestori archivio

Note: Capitolo completo
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 501.63 kB
Formato Adobe PDF
501.63 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1616251
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact