Title :
Disease-Gene Association Using a Genetic Algorithm
Author :
Tahmasebipour, Koosha ; Houghten, Sheridan
Author_Institution :
Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
Abstract :
Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease-gene association problem. Nowadays, it is clear that many of genetic diseases are not consequence of defects in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, most of genetic diseases occur as a result of various genes working in sync with each other in a single or several biological module (s). Using a genetic algorithm, we have devised a computational approach for disease-gene association. As a proof of concept, we apply this method to the problem of identifying genes involved in breast cancer.
Keywords :
cancer; complex networks; genetic algorithms; genetics; health care; medical computing; breast cancer; complex network; computational methods; defect consequence; disease phenotype; disease-gene association; genetic algorithm; genetic component reflection; genetic diseases; human health; single gene; Communities; Diseases; Genetic algorithms; Genetics; Proteins; Sociology; Statistics; Community Identification; Complex Networks; Disease-Gene Association; Genetic Algorithms;
Conference_Titel :
Bioinformatics and Bioengineering (BIBE), 2014 IEEE International Conference on
Conference_Location :
Boca Raton, FL
DOI :
10.1109/BIBE.2014.38