Linked Life Data

16646788

Source:http://linkedlifedata.com/resource/pubmed/id/16646788

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2006-5-1
pubmed:abstractText
Standard laboratory classification of the plasma cell dyscrasia monoclonal gammopathy of undetermined significance (MGUS) and the overt plasma cell neoplasm multiple myeloma (MM) is quite accurate, yet, for the most part, biologically uninformative. Most, if not all, cancers are caused by inherited or acquired genetic mutations that manifest themselves in altered gene expression patterns in the clonally related cancer cells. Microarray technology allows for qualitative and quantitative measurements of the expression levels of thousands of genes simultaneously, and it has now been used both to classify cancers that are morphologically indistinguishable and to predict response to therapy. It is anticipated that this information can also be used to develop molecular diagnostic models and to provide insight into mechanisms of disease progression, e.g., transition from healthy to benign hyperplasia or conversion of a benign hyperplasia to overt malignancy. However, standard data analysis techniques are not trivial to employ on these large data sets. Methodology designed to handle large data sets (or modified to do so) is needed to access the vital information contained in the genetic samples, which in turn can be used to develop more robust and accurate methods of clinical diagnostics and prognostics.
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1544-6115
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
Article10
pubmed:dateRevised
2006-12-4
pubmed:year
2004
pubmed:articleTitle
Evaluation of multiple models to distinguish closely related forms of disease using DNA microarray data: an application to multiple myeloma.
pubmed:affiliation
Pomona College. hardin@pomona.edu
pubmed:publicationType
Journal Article