TY - GEN
T1 - Exploring fibrotic disease networks to identify common molecular mechanisms with IPF
AU - Karatzas, Evangelos
AU - Delis, Alex
AU - Kolios, George
AU - Spyrou, George M.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Fibrotic diseases constitute incurable maladies that affect a large portion of the population. Idiopathic Pulmonary Fibrosis is one of the most common, and thus studied, fibrotic diseases. Common ground among all fibrotic diseases is the uncontrollable fibrogenesis which is responsible for accumulated damage in the susceptible tissues. The plethora and complexity of the underlying mechanisms of fibrotic diseases account for the lack of regimens. Hence it is highly likely that a combination of drugs is required in order to counter every perturbation. In this study, we seek to identify common biological mechanisms and characteristics of fibrotic diseases, based on information acquired from biological databases, while we focus on Idiopathic Pulmonary Fibrosis. We also try to predict links between molecular data and their respective fibrotic phenotypes. We finally construct phenotypic and molecular networks, visualize them and apply a clustering algorithm on each network to identify fibrotic diseases that are close to Idiopathic Pulmonary Fibrosis.
AB - Fibrotic diseases constitute incurable maladies that affect a large portion of the population. Idiopathic Pulmonary Fibrosis is one of the most common, and thus studied, fibrotic diseases. Common ground among all fibrotic diseases is the uncontrollable fibrogenesis which is responsible for accumulated damage in the susceptible tissues. The plethora and complexity of the underlying mechanisms of fibrotic diseases account for the lack of regimens. Hence it is highly likely that a combination of drugs is required in order to counter every perturbation. In this study, we seek to identify common biological mechanisms and characteristics of fibrotic diseases, based on information acquired from biological databases, while we focus on Idiopathic Pulmonary Fibrosis. We also try to predict links between molecular data and their respective fibrotic phenotypes. We finally construct phenotypic and molecular networks, visualize them and apply a clustering algorithm on each network to identify fibrotic diseases that are close to Idiopathic Pulmonary Fibrosis.
KW - Biological networks
KW - Clustering
KW - Common biological pathways
KW - Fibrotic diseases
KW - Idiopathic Pulmonary Fibrosis
KW - Network visualization
UR - http://www.scopus.com/inward/record.url?scp=85078573742&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078573742&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2019.00022
DO - 10.1109/BIBE.2019.00022
M3 - Conference contribution
AN - SCOPUS:85078573742
T3 - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
SP - 72
EP - 77
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 -