Identifying Complex Biological Interactions based on Categorical Gene Expression Data

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

@InProceedings{Goertzel:2006:CEC,
  author =       "Ben Goertzel and Cassio Pennachin and 
                 Lucio {de Souza Coelho} and Mauricio Mudado",
  title =        "Identifying Complex Biological Interactions based on
                 Categorical Gene Expression Data",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "5583--5590",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, poster",
  ISBN =         "0-7803-9487-9",
  URL =          "http://www.biomind.com/docs/WCCI_EC_feb06_06_fixed_v2.pdf",
  DOI =          "doi:10.1109/CEC.2006.1688477",
  size =         "8 pages",
  abstract =     "A novel method, MUTIC ( Clustering), is described for
                 identifying complex interactions between genes or
                 gene-categories based on gene expression data. The
                 method deals with binary categorical data, which
                 consists of a set of gene expression profiles divided
                 into two biologically meaningful categories. It does
                 not require data from multiple time points. Gene
                 expression profiles are represented by feature vectors
                 whose component features are either gene expression
                 values, or averaged expression values corresponding to
                 Gene Ontology or Protein Information Resource
                 categories. A supervised learning algorithm (genetic
                 programming) is used to learn an ensemble of
                 classification models distinguishing the two categories
                 based on the feature vectors corresponding to their
                 members. Each feature is associated with a model usage
                 vector, which has an entry for each high-quality
                 classification model found, indicating whether or not
                 the feature was used in that model. These usage vectors
                 are then clustered using a variant of hierarchical
                 clustering called Omniclust. The result is a set of
                 model-usage-based clusters, in which features are
                 gathered together if they are often considered together
                 by classification models which may be because they are
                 co-expressed, or may be for subtler reasons involving
                 multi-gene interactions. The MUTIC method is
                 illustrated via applying it to a dataset regarding gene
                 expression in human brains of various ages. Compared to
                 traditional expression-based clustering, MUTIC yields
                 clusters that have higher mathematical quality (in the
                 sense of homogeneity and separation) and also yield
                 novel insights into the underlying biological
                 processes.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Ben Goertzel Cassio Pennachin Lucio de Souza Coelho Mauricio Mudado

Citations