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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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