Linked Life Data

15468755

Source:http://linkedlifedata.com/resource/pubmed/id/15468755

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2004-10-7
pubmed:abstractText
Recent advances in molecular biology (e.g., cDNA microarray technology) enables the simultaneous monitoring of the expression level of thousands of genes. Due to the massive amount of complex data generated, sophisticated statistical approaches are necessary in order to properly address the experimental investigation. In this paper, we present statistical analysis of cDNA microarray data derived from bone regeneration experiments. Several interesting features from these data distinguish it from commonly used microarray experiment (i.e., separate hybridization of mRNA samples from reference and experimental tissues, selectively spotted cDNA sequences and 1060 systematically selected blank spots included in each array). Using this data set, we propose new methods for bioinformatic data normalization, as well as the modification and application of various other published methods in order to identify co-regulated gene expression patterns during the healing of a bone fracture. The proposed normalization methods perform effectively to eliminate the variations with a simple algorithm. Results from our cluster analysis revealed several clusters having distinct gene expression patterns during fracture healing. Our simulation study supports the reliability of the proposed methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1054-3406
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
607-28
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Statistical approaches in the analysis of gene expression data derived from bone regeneration specific cDNA microarrays.
pubmed:affiliation
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA. jooj@nhlbi.nih.gov
pubmed:publicationType
Journal Article