학술논문

Computational Identification of Metabolites for Pathways Related to Huntington's Disease
Document Type
Conference
Source
2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) Bioinformatics and Bioengineering (BIBE), 2019 IEEE 19th International Conference on. :832-837 Oct, 2019
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Metabolomics
Systematics
Conferences
Systems biology
Neurons
Data integration
Bioinformatics
Huntington's disease, Metabolomics, Systems Bioinformatics, PathwayConnector
Language
ISSN
2471-7819
Abstract
Huntington's disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. Systems Bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HD-related pathways, which were previously identified from our previous work on multi-source data integration These include: i) creation of pathway-to-pathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathway-to-pathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.