- author = "W. B. Langdon and A. P. Harrison",
- title = "Evolving {DNA} motifs to Predict {GeneChip} Probe Performance",
- journal = "Algorithms in Molecular Biology",
- year = "2009",
- volume = "4",
- number = "6",
- month = "19 " # mar,
- keywords = "genetic algorithms, genetic programming, Bioinformatics, Affymetrix GeneChip, strongly typed genetic programming, grammars, regular expressions",
- ISSN = "1748-7188",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_amb.pdf",
-
URL = "
http://www.almob.org/content/4/1/6",
-
DOI = "
doi:10.1186/1748-7188-4-6",
- size = "16 pages",
-
abstract = "Background:
Affymetrix High Density Oligonuclotide Arrays (HDONA) simultaneously measure expression of thousands of genes using millions of probes. We use correlations between measurements for the same gene across 6685 human tissue samples from NCBI's GEO database to indicated the quality of individual HG-U133A probes. Low correlation indicates a poor probe.
Results:
Regular expressions can be automatically created from a Backus-Naur form (BNF) context-free grammar using strongly typed genetic programming.
Conclusions:
The automatically produced motif is better at predicting poor DNA sequences than an existing human generated RE, suggesting runs of Cytosine and Guanine and mixtures should all be avoided.",
- notes = "Based on \cite{langdon:2008:PPSN}. PMID: 19298675 PMCID: PMC2679018",
