Application of Machine-Learning Methods to Understand Gene Expression Regulation

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Chao Cheng and William P. Worzel",
  title =        "Application of Machine-Learning Methods to Understand
                 Gene Expression Regulation",
  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 =        "1--15",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Support
                 Vector Machine, SVM, Random forest, GP, ENCODE,
                 modENCODE, Transcription Factor (TF), Histone
                 modification, ChIP-Chip, ChIP-seq, RNA-seq",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_1",
  abstract =     "With the development and application of
                 high-throughput technologies, an enormous amount of
                 biological data has been produced in the past few
                 years. These large-scale datasets make it possible and
                 necessary to implement machine learning techniques for
                 mining biological insights. In this chapter, we
                 describe several examples to show how machine learning
                 approaches are used to elucidate the mechanism of
                 transcriptional regulation mediated by transcription
                 factors and histone modifications. We demonstrate that
                 machine learning provides powerful tools to
                 quantitatively relate gene expression with
                 transcription factor binding and histone modifications,
                 to identify novel regulatory DNA elements in the
                 genomes, and to predict gene functions. We also discuss
                 the advantages and limitations of genetic programming
                 in analysing and processing biological data.",
  notes =        "

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

Genetic Programming entries for Chao Cheng William P Worzel