Molecular networks in Network Medicine: Development and applications

Edwin K. Silverman*, Harald H. H. W. Schmidt, Eleni Anastasiadou, Lucia Altucci, Marco Angelini, Lina Badimon, Jean-Luc Balligand, Giuditta Benincasa, Giovambattista Capasso, Federica Conte, Antonella Di Costanzo, Lorenzo Farina, Giulia Fiscon, Laurent Gatto, Michele Gentili, Joseph Loscalzo, Cinzia Marchese, Claudio Napoli, Paola Paci, Manuela PettiJohn Quackenbush, Paolo Tieri, Davide Viggiano, Gemma Vilahur, Kimberly Glass, Jan Baumbach

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

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Abstract

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in ideNetwork Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseasesntifying key genes within genetic association regions, and limited applications to human diseases
Original languageEnglish
Article numbere1489
Number of pages38
JournalWiley Interdisciplinary Reviews-Systems Biology and Medicine
Volume12
Issue number6
DOIs
Publication statusPublished - Nov 2020

Keywords

  • big data
  • molecular networks
  • network medicine
  • PROTEIN-INTERACTION NETWORKS
  • GENE REGULATORY NETWORKS
  • SYSTEMS BIOLOGY
  • EXPRESSION DATA
  • CELL-CULTURE
  • EPIGENETIC REGULATION
  • INTEGRATED ANALYSIS
  • HUMAN GENOME
  • STEM-CELL
  • VISUALIZATION

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