TY - GEN
T1 - Computational identification of metabolites for pathways related to huntington's disease
AU - Christodoulou, Christiana
AU - Minadakis, George
AU - Demetriou, Christiana
AU - Zamba-Papanicolaou, Eleni
AU - Spyrou, George
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Huntington's disease
KW - Metabolomics
KW - PathwayConnector
KW - Systems Bioinformatics
UR - http://www.scopus.com/inward/record.url?scp=85078026285&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2019.00155
DO - 10.1109/BIBE.2019.00155
M3 - Conference contribution
AN - SCOPUS:85078026285
T3 - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
SP - 832
EP - 837
BT - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Y2 - 28 October 2019 through 30 October 2019
ER -