TY  - JOUR
TI  - Evaluation of Sub-Network search programs in epilepsy-related GWAS
dataset
AB  - The active sub-network detection aims to find a group of interconnected
genes of disease-related genes in a protein-protein interaction network.
In recent years, several algorithms have been developed for this
problem. In this study, the analysis of disease-specific sub-network
identification programs is evaluated using epilepsy data set. Under the
same conditions and with the same data set, 9 different programs are
run and results of their Greedy algorithm, Genetic algorithm, Simulated
Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm,
MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein
Complex Detection using Weighted Clustering Coefficient) algorithm
are shown. The top-scoring 5 modules of each program, are compared
using fold enrichment analysis and normalized mutual information.
Also, the identified subnetworks are functionally enriched using a
hypergeometric test, and hence, disease-associated biological pathways
are identified. In addition, running times and features of the programs
are comparatively evaluated
AU  - GUNGOR, Burcu BAKİR
AU  - DEDETURK, Beyhan ADANUR
DO  - 10.5505/pajes.2021.56424
PY  - 2022
JO  - Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
VL  - 28
IS  - 2
SN  - 2147-5881
SP  - 292
EP  - 298
DB  - TRDizin
UR  - http://search/yayin/detay/524956
ER  -