Evolving GeneChip correlation predictors on parallel graphics hardware

Author

Langdon, W.B.

Author_Institution

Math. & Biol. Sci., Univ. of Essex, Colchester

fYear

2008

fDate

1-6 June 2008

Firstpage

4151

Lastpage

4156

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CEC.2008.4631364

Filename

4631364