TY - GEN
T1 - Exploring the genetics underlying autoimmune diseases with network analysis and link prediction
AU - Alanis Lobato, Gregorio
AU - Cannistraci, Carlo
AU - Ravasi, Timothy
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2014/2
Y1 - 2014/2
N2 - Ever since the first Genome Wide Association Study (GWAS) was carried out we have seen an important number of discoveries of biological and clinical relevance. However, there are some scientists that consider that these research outcomes and their utility are far from what was expected from this experimental design. We instead believe that the thousands of genetic variants associated with complex disorders by means of GWASs are an extremely valuable source of information that needs to be mined in a different way. Based on this philosophy, we followed a holistic perspective to analyze GWAS data and explored the structural properties of the network representation of one of these datasets with the aim to advance our understanding of the genetic intricacies underlying autoimmune human diseases. The simplicity, computational efficiency and precision of the tools proposed in this paper represent a new means to address GWAS data and contribute to the better exploitation of these rich sources of information. © 2014 IEEE.
AB - Ever since the first Genome Wide Association Study (GWAS) was carried out we have seen an important number of discoveries of biological and clinical relevance. However, there are some scientists that consider that these research outcomes and their utility are far from what was expected from this experimental design. We instead believe that the thousands of genetic variants associated with complex disorders by means of GWASs are an extremely valuable source of information that needs to be mined in a different way. Based on this philosophy, we followed a holistic perspective to analyze GWAS data and explored the structural properties of the network representation of one of these datasets with the aim to advance our understanding of the genetic intricacies underlying autoimmune human diseases. The simplicity, computational efficiency and precision of the tools proposed in this paper represent a new means to address GWAS data and contribute to the better exploitation of these rich sources of information. © 2014 IEEE.
UR - http://hdl.handle.net/10754/564885
UR - http://ieeexplore.ieee.org/document/6783232/
UR - http://www.scopus.com/inward/record.url?scp=84900815089&partnerID=8YFLogxK
U2 - 10.1109/MECBME.2014.6783232
DO - 10.1109/MECBME.2014.6783232
M3 - Conference contribution
SN - 9781479947997
SP - 167
EP - 170
BT - 2nd Middle East Conference on Biomedical Engineering
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -
