Home // DBKDA 2018, The Tenth International Conference on Advances in Databases, Knowledge, and Data Applications // View article
Authors:
Dimitar Hristovski
Gaber Bergant
Andrej Kastrin
Borut Peterlin
Keywords: Literature-based discovery; Next-generation sequencing; Genomic data analysis; Semantic MEDLINE; Graph database; Neo4j
Abstract:
The arrival of high-throughput sequencing technologies in routine diagnostic medicine has enabled the large scale use of these technologies; however, the challenges of interpreting the results for diagnostic purposes has recently become evident. For this reason, we aim to develop a bioinformatics tool for clinical genetics diagnostics support. We gathered the data for this project from several different sources, including semantic relations extracted with SemRep from the MEDLINE bibliographic database, clinical phenotype observations from clinical geneticists and finally genotype data produced by Next-Generation Sequencing (NGS) annotated with population data and several theoretical pathogenicity prediction algorithms. We stored this data in a Neo4j graph database and employed it using a closed discovery approach to Literature-Based Discovery (LBD) as a complementary method in diagnostic NGS data analysis. All algorithms were implemented using the Cypher query language. The goal of the study was first to determine the usability of graph databases to represent heterogeneous clinical genomic data and secondly to determine the potential benefits of using LBD as a complementary approach in a diagnostic setting using NGS.
Pages: 68 to 70
Copyright: Copyright (c) IARIA, 2018
Publication date: May 20, 2018
Published in: conference
ISSN: 2308-4332
ISBN: 978-1-61208-637-8
Location: Nice, France
Dates: from May 20, 2018 to May 24, 2018