Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{Moore:2011:GPTP,

• author = "Jason H. Moore and Douglas P. Hill and Jonathan M. Fisher and Nicole Lavender and La Creis Kidd",
• title = "Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer",
• booktitle = "Genetic Programming Theory and Practice IX",
• year = "2011",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore",
• series = "Genetic and Evolutionary Computation",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• publisher = "Springer",
• chapter = "9",
• pages = "153--171",
• keywords = "genetic algorithms, genetic programming, Computational Evolution, Genetic Epidemiology, epistasis, Prostate Cancer, Visualisation",
• isbn13 = "978-1-4614-1769-9",
• DOI = "doi:10.1007/978-1-4614-1770-5_9",
• abstract = "The paradigm of identifying genetic risk factors for common human diseases by analysing one DNA sequence variation at a time is quickly being replaced by research strategies that embrace the multivariate complexity of the genotype to phenotype mapping relationship that is likely due, in part, to nonlinear interactions among many genetic and environmental factors. Embracing the complexity of common diseases such as cancer requires powerful computational methods that are able to model nonlinear interactions in high-dimensional genetic data. Previously, we have addressed this challenge with the development of a computational evolution system (CES) that incorporates greater biological realism than traditional artificial evolution methods, such as genetic programming. Our 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 predisposition. Further, we have shown that the efficacy of CES is improved dramatically when the system is provided with statistical expert knowledge, derived from a family of machine learning techniques known as Relief, or biological expert knowledge, derived from sources such as protein-protein interaction databases. 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 3D visualization methods to identify interesting patterns in CES results. Information extracted from the visualization through human-computer interaction are then provide as expert knowledge to new CES runs in a cascading framework. We present a CES-derived multivariate classifier and provide a statistical and biological interpretation in the context of prostate cancer prediction. The incorporation of human-computer interaction into CES provides a first step towards an interactive discovery system where the experts can be embedded in the computational discovery process. Our working hypothesis is that this type of human-computer interaction will provide more useful results for complex problem solving than the traditional black box machine learning approach.",
• notes = "part of \cite{Riolo:2011:GPTP}",
• affiliation = "Dartmouth Medical School, One Medical Center Drive, HB7937, Lebanon, NH 03756, USA",

}

Genetic Programming entries for Jason H Moore Douglas P Hill Jonathan M Fisher Nicole A Lavender La Creis Renee Kidd

Citations