- author = "W. B. Langdon and B. F. Buxton",
- title = "Genetic Programming for Mining {DNA} Chip data from Cancer Patients",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2004",
- volume = "5",
- number = "3",
- pages = "251--257",
- month = sep,
- keywords = "genetic algorithms, genetic programming, attribute selection, leave one out cross fold validation, data mining, machine learning, classification, evolutionary algorithms, DNA chip gene expression, predicting cancer",
- ISSN = "1389-2576",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_dnachip.pdf",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_dnachip.ps.gz",
-
URL = "
https://rdcu.be/cArOk",
-
DOI = "
doi:10.1023/B:GENP.0000030196.55525.f7",
-
data_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/Pomeroy_et_al_0G04850_11142001_datasets",
- size = "8 pages",
- abstract = "In machine learning terms DNA (gene) chip data is unusual in having thousands of attributes (the gene expression values) but few (<100) records (the patients). A GP based method for both feature selection and generating simple models based on a few genes is demonstrated on cancer data.",
-
notes = "Article ID: 5272972
Oct 2021 Pomeroy_et_al_0G04850_11142001_datasets.zip missing from www-genome.wi.mit.edu our files in data_url",
