Title :
Evolving GeneChip correlation predictors on parallel graphics hardware
Author_Institution :
Math. & Biol. Sci., Univ. of Essex, Colchester
Abstract :
A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBIpsilas GEO database. These concordances are used as machine learning training data for genetic programming running on a Linux PC with a RapidMind OpenGL GLSL backend. GPGPU is used to identify technological factors influencing high density oligonuclotide arrays (HDONA) performance. GP suggests mismatch (PM/MM) and adenosine/guanine ratio influence microarray quality. Initial results hint that Watson-Crick probe self hybridisation or folding is not important. Under GPGPGPU an nVidia GeForce 8800 GTX interprets 300 million GP primitives/second (300 MGPops, approx 8 GFLOPS).
Keywords :
Linux; biology computing; computer graphics; genetic algorithms; genetics; learning (artificial intelligence); Affymetrix HG-U133A probesets; GPU; GeneChip correlation predictors; Linux PC; NCBI GEO database; RapidMind OpenGL GLSL backend; adenosine-guanine ratio; genetic programming; high density oligonuclotide arrays performance; machine learning training data; nVidia GeForce 8800 GTX; parallel graphics hardware; DNA; Databases; Genetic programming; Graphics; Hardware; Humans; Machine learning; Probes; Sequences; Training data;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631364
