Bioinformatics Analysis of Omics Data Towards Cancer Diagnosis and Prognosis

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jianjun Yu",
  title =        "Bioinformatics Analysis of Omics Data Towards Cancer
                 Diagnosis and Prognosis",
  school =       "University of Michigan",
  year =         "2007",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-549-30549-1",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "196 pages",
  abstract =     "Despite important advances in cancer research in
                 recent decades, an accurate diagnosis and prognosis of
                 cancer remains a formidable challenge to date. In this
                 dissertation, several bioinformatics analyses have been
                 developed for identifying new diagnostic/prognostic
                 signatures using datasets derived from recent
                 high-throughput screening techniques including DNA and
                 protein microarray. In the first analysis we derived an
                 outcome signature from estrogen signalling pathway to
                 predict breast cancer prognosis. This signature
                 successfully predicted patient outcome in multiple
                 patient cohorts as well as ER+ and tamoxifen-treated
                 sub-cohorts. The second part of my thesis focused on
                 applying genetic programming for cancer classification.
                 This approach can automatically select a handful of
                 discriminative genes from gene expression data and
                 produce comprehensible yet efficient rule-based
                 classifiers. In the third analysis, we developed
                 non-invasive diagnostic tools for prostate cancer
                 diagnosis. Two different signatures were yielded from
                 phage peptide microarray system and q-PCR urinary data,
                 respectively. These signatures have the potential to
                 improve specificity and sensitivity of prostate cancer
                 diagnosis. Last, an integrative model was developed for
                 culling a molecular signature of metastatic progression
                 in prostate cancer from proteomic and transcriptomic
                 data. Differential proteomic alterations between
                 localised and metastatic prostate cancer, which were
                 concordant with transcriptomic data, served as a
                 predictor of clinical outcome in prostate cancer. This
                 signature was also predictive of clinical outcome on
                 other solid tumours, suggesting common molecular
                 machinery in aggressive neoplasms. In summary, these
                 bioinformatics analyses of cancer 'omics' data have led
                 to several important findings that may ameliorate
                 cancer diagnosis and prognosis.",
  notes =        "The genetic programming part should be in Chapter 4
                 (page 69)

                 Chair: Arul M. Chinnaiyan",

Genetic Programming entries for Jianjun Yu