Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InCollection{Moore:2012:GPTP,

• author = "Jason H. Moore and Douglas P. Hill and Arvis Sulovari and LaCreis Kidd",
• title = "Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing",
• booktitle = "Genetic Programming Theory and Practice X",
• year = "2012",
• series = "Genetic and Evolutionary Computation",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore",
• publisher = "Springer",
• chapter = "7",
• pages = "87--101",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• keywords = "genetic algorithms, genetic programming, Computational evolution, Genetic epidemiology, Epistasis, Gene-gene interactions",
• isbn13 = "978-1-4614-6845-5",
• URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_7",
• DOI = "doi:10.1007/978-1-4614-6846-2_7",
• abstract = "Given infinite time, humans would progress through modelling complex data in a manner that is dependent on prior expert knowledge. The goal of the present study is make extensions and enhancements to a computational evolution system (CES) that has the ultimate objective of tinkering with data as a human would. This is accomplished by providing flexibility in the model-building process and a meta-layer that learns how to generate better models. The key to the CES system is the ability to identify and exploit expert knowledge from biological databases or prior analytical results. Our prior results have demonstrated that CES is capable of efficiently navigating these large and rugged fitness landscapes toward the discovery of biologically meaningful genetic models of disease. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical or biological expert knowledge. The goal of the present study was to apply CES to the genetic analysis of prostate cancer aggressiveness in a large sample of European Americans. We introduce here the use of Pareto-optimisation to help address overfitting in the learning system. We further introduce a post-processing step that uses hierarchical cluster analysis to generate expert knowledge from the landscape of best models and their predictions across patients. We find that the combination of Pareto-optimization and post-processing of results greatly improves the genetic analysis of prostate cancer.",
• notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013",

}

Genetic Programming entries for Jason H Moore Douglas P Hill Arvis Sulovari La Creis Renee Kidd

Citations