TY - JOUR
T1 - Molecular networks in Network Medicine
T2 - Development and applications
AU - Silverman, Edwin K.
AU - Schmidt, Harald H. H. W.
AU - Anastasiadou, Eleni
AU - Altucci, Lucia
AU - Angelini, Marco
AU - Badimon, Lina
AU - Balligand, Jean-Luc
AU - Benincasa, Giuditta
AU - Capasso, Giovambattista
AU - Conte, Federica
AU - Di Costanzo, Antonella
AU - Farina, Lorenzo
AU - Fiscon, Giulia
AU - Gatto, Laurent
AU - Gentili, Michele
AU - Loscalzo, Joseph
AU - Marchese, Cinzia
AU - Napoli, Claudio
AU - Paci, Paola
AU - Petti, Manuela
AU - Quackenbush, John
AU - Tieri, Paolo
AU - Viggiano, Davide
AU - Vilahur, Gemma
AU - Glass, Kimberly
AU - Baumbach, Jan
PY - 2020/11
Y1 - 2020/11
N2 - 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
AB - 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
KW - big data
KW - molecular networks
KW - network medicine
KW - PROTEIN-INTERACTION NETWORKS
KW - GENE REGULATORY NETWORKS
KW - SYSTEMS BIOLOGY
KW - EXPRESSION DATA
KW - CELL-CULTURE
KW - EPIGENETIC REGULATION
KW - INTEGRATED ANALYSIS
KW - HUMAN GENOME
KW - STEM-CELL
KW - VISUALIZATION
U2 - 10.1002/wsbm.1489
DO - 10.1002/wsbm.1489
M3 - Review article
C2 - 32307915
VL - 12
JO - Wiley Interdisciplinary Reviews-Systems Biology and Medicine
JF - Wiley Interdisciplinary Reviews-Systems Biology and Medicine
SN - 1939-5094
IS - 6
M1 - e1489
ER -