Using sequence DNA chips data to Mining and Diagnosing Cancer Patients

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Zakaria Suliman Zubi and Marim Aboajela Emsaed",
  title =        "Using sequence DNA chips data to Mining and Diagnosing
                 Cancer Patients",
  journal =      "International Journal of Computers",
  year =         "2010",
  volume =       "4",
  number =       "4",
  month =        "201--214",
  keywords =     "genetic algorithms, genetic programming, DNA
                 micro-array, Data Mining, Sequence Mining, Biological
                 Database, Clustering, Classification, K-means.",
  ISSN =         "1998-4308",
  URL =          "",
  URL =          "",
  size =         "14 pages",
  abstract =     "Deoxyribonucleic acid (DNA) micro-arrays present a
                 powerful means of observing thousands of gene terms
                 levels at the same time. They consist of high
                 dimensional datasets, which challenge conventional
                 clustering methods. The data's high dimensionality
                 calls for Self Organizing Maps (SOMs) to cluster DNA
                 micro-array data. The DNA micro-array data set are
                 stored in huge biological databases for several
                 purposes [1]. The proposed methods are based on the
                 idea of selecting a gene subset to distinguish all
                 classes, it will be more effective to solve a
                 multi-class problem, and we will propose a genetic
                 programming (GP) based approach to analyse multi-class
                 micro-array datasets. This biological dataset will be
                 derived from multiple biological databases. The
                 procedure responsible for extracting datasets called
                 DNA-Aggregator. We will design a biological aggregator,
                 which aggregates various datasets via DNA micro-array
                 community-developed ontology based upon the concept of
                 semantic Web for integrating and exchanging biological
                 data. Our aggregator is composed of modules that
                 retrieve the data from various biological databases. It
                 will also enable queries by other applications to
                 recognise the genes. The genes will be categorised in
                 groups based on a classification method, which collects
                 similar expression patterns. Using a clustering method
                 such as k-mean is required either to discover the
                 groups of similar objects from the biological database
                 to characterize the underlying data distribution.",
  notes =        "see also acmid = {1895290}

                 Sirte University, Faculty of Science, Computer Science
                 Department, Sirte, P.O Box 727, Libya,

                 Alfateh University, Faculty of Science, Computer
                 Science Department, Tripoli, Libya, P,O Box


Genetic Programming entries for Zakaria Suliman Zubi Marim Aboajela Emsaed