- author = "W. B. Langdon",
- title = "Evolving {GeneChip} Correlation Predictors on Parallel Graphics Hardware",
- booktitle = "2008 IEEE World Congress on Computational Intelligence",
- year = "2008",
- editor = "Jun Wang",
- pages = "4152--4157",
- address = "Hong Kong",
- month = "1-6 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, GPU, bioinformatics, microarray, performance",
- isbn13 = "978-1-4244-1823-7",
- file = "EC0881.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU2.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU2.ps.gz",
-
DOI = "
10.1109/CEC.2008.4631364",
- size = "6 pages",
- abstract = "A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBI's 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).",
-
notes = "RapidMind OpenGL Shading Language (GLSL) back end
WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",
