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A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Nicola Capuano, Pasquale Foggia, Luca Greco, Pierluigi Ritrovato
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 22
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.297141
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MLA

Capuano, Nicola, et al. "A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases." IJSWIS vol.18, no.1 2022: pp.1-22. http://doi.org/10.4018/IJSWIS.297141

APA

Capuano, N., Foggia, P., Greco, L., & Ritrovato, P. (2022). A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-22. http://doi.org/10.4018/IJSWIS.297141

Chicago

Capuano, Nicola, et al. "A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-22. http://doi.org/10.4018/IJSWIS.297141

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Abstract

Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.