GP-Pi: Using Genetic Programming with Penalization and Initialization on Genome-Wide Association Study

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@InProceedings{conf/icaisc/Sze-ToLTWLTL13,

• author = "Ho-Yin Sze-To and Kwan-Yeung Lee and Kai-Yuen Tso and Man Hon Wong and Kin-Hong Lee and Nelson L. S. Tang and Kwong-Sak Leung",
• title = "{GP-Pi}: Using Genetic Programming with Penalization and Initialization on Genome-Wide Association Study",
• bibdate = "2013-06-07",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icaisc/icaisc2013-2.html#Sze-ToLTWLTL13",
• booktitle = "Artificial Intelligence and Soft Computing - 12th International Conference, {ICAISC} 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part {II}",
• publisher = "Springer",
• year = "2013",
• volume = "7895",
• editor = "Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek M. Zurada",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-38609-1",
• pages = "330--341",
• series = "Lecture Notes in Computer Science",
• URL = "http://dx.doi.org/10.1007/978-3-642-38610-7",
• DOI = "doi:10.1007/978-3-642-38610-7_31",
• abstract = "The advancement of chip-based technology has enabled the measurement of millions of DNA sequence variations across the human genome. Experiments revealed that high-order, but not individual, interactions of single nucleotide polymorphisms (SNPs) are responsible for complex diseases such as cancer. The challenge of genome-wide association studies (GWASs) is to sift through high-dimensional datasets to find out particular combinations of SNPs that are predictive of these diseases. Genetic Programming (GP) has been widely applied in GWASs. It serves two purposes: attribute selection and/or discriminative modelling. One advantage of discriminative modelling over attribute selection lies in interpretability. However, existing discriminative modelling algorithms do not scale up well with the increase in the SNP dimension. Here, we have developed GP-Pi. We have introduced a penalising term in the fitness function to penalise trees with common SNPs and an initialiser which uses expert knowledge to seed the population with good attributes. Experimental results on simulated data suggested that GP-Pi outperforms GPAS with statistically significance. GP-Pi was further evaluated on a real GWAS dataset of Rheumatoid Arthritis, obtained from the North American Rheumatoid Arthritis Consortium. Our results, with potential new discoveries, are found to be consistent with literature.",

}

Genetic Programming entries for Ho-Yin Sze-To Kwan-Yeung Lee Kai-Yuen Tso Man Hon Wong Kin-Hong Lee Nelson L S Tang Kwong-Sak Leung

Citations