Identification of Novel Genetic Models of Glaucoma Using the ``EMERGENT'' Genetic Programming-Based Artificial Intelligence System

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Jason H. Moore and Casey S. Greene and 
                 Douglas P. Hill",
  title =        "Identification of Novel Genetic Models of Glaucoma
                 Using the {``EMERGENT''} Genetic Programming-Based
                 Artificial Intelligence System",
  booktitle =    "Genetic Programming Theory and Practice XII",
  year =         "2014",
  editor =       "Rick Riolo and William P. Worzel and Mark Kotanchek",
  series =       "Genetic and Evolutionary Computation",
  pages =        "17--35",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Exploratory
                 modelling for extracting relationships using genetic
                 and evolutionary navigation techniques, Artificial
                 intelligence, Glaucoma",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_2",
  abstract =     "The genetic basis for primary open-angle glaucoma
                 (POAG) is not yet understood but is likely the result
                 of many interacting genetic variants that influence
                 risk in the context of our local ecology. The
                 complexity of the genotype to phenotype mapping
                 relationship for common diseases like POAG necessitates
                 analytical approaches that move beyond parametric
                 statistical methods such as logistic regression that
                 assume a particular mathematical model. This is
                 particularly important in the era of big data where it
                 is routine to collect and analyse data sets with
                 hundreds of thousands of measured genetic variants in
                 thousands of human subjects. We introduce here the
                 Exploratory Modelling for Extracting Relationships
                 using Genetic and Evolutionary Navigation Techniques
                 (EMERGENT) algorithm as an artificial intelligence
                 approach to the genetic analysis of common human
                 diseases. EMERGENT builds models of genetic variation
                 from lists of mathematical functions using a form of
                 genetic programming called computational evolution. A
                 key feature of the system is the ability to use
                 pre-processed expert knowledge giving it the ability to
                 explore model space much as a human would. We describe
                 this system in detail and then apply it to the genetic
                 analysis of POAG in the Glaucoma Gene Environment
                 Initiative (GLAUGEN) study that included approximately
                 1,272 subjects with the disease and 1057 healthy
                 controls. A total of 657,366 single-nucleotide
                 polymorphisms (SNPs) from across the human genome were
                 measured in these subjects and available for analysis.
                 Analysis using the EMERGENT framework revealed a best
                 model consisting of six SNPs that map to at least six
                 different genes. Two of these genes have previously
                 been associated with POAG in several studies. The
                 others represent new hypotheses about the genetic basis
                 of POAG. All of the SNPs are involved in non-additive
                 gene-gene interactions. Further, the six genes are all
                 directly or indirectly related through biological
                 interactions to the vascular endothelial growth factor
                 (VEGF) gene that is an actively investigated drug
                 target for POAG. This study demonstrates the routine
                 application of an artificial intelligence-based system
                 for the genetic analysis of complex human diseases.",
  notes =        "

                 Part of \cite{Riolo:2014:GPTP} published after the
                 workshop in 2015",

Genetic Programming entries for Jason H Moore Casey S Greene Douglas P Hill