Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2003-3-24
pubmed:abstractText
MOTIVATION: Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance. RESULTS: A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
22
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
571-8
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed-meshheading:12651714-Acute Disease, pubmed-meshheading:12651714-Algorithms, pubmed-meshheading:12651714-Child, pubmed-meshheading:12651714-Child, Preschool, pubmed-meshheading:12651714-Gene Expression Profiling, pubmed-meshheading:12651714-Gene Expression Regulation, Neoplastic, pubmed-meshheading:12651714-Humans, pubmed-meshheading:12651714-Infant, pubmed-meshheading:12651714-Infant, Newborn, pubmed-meshheading:12651714-Leukemia, Myeloid, pubmed-meshheading:12651714-Lymphoma, Non-Hodgkin, pubmed-meshheading:12651714-Models, Genetic, pubmed-meshheading:12651714-Models, Statistical, pubmed-meshheading:12651714-Neoplasms, pubmed-meshheading:12651714-Neuroblastoma, pubmed-meshheading:12651714-Oligonucleotide Array Sequence Analysis, pubmed-meshheading:12651714-Precursor Cell Lymphoblastic Leukemia-Lymphoma, pubmed-meshheading:12651714-Principal Component Analysis, pubmed-meshheading:12651714-Rhabdomyosarcoma, pubmed-meshheading:12651714-Sarcoma, Ewing, pubmed-meshheading:12651714-Tumor Markers, Biological
pubmed:year
2003
pubmed:articleTitle
PCA disjoint models for multiclass cancer analysis using gene expression data.
pubmed:affiliation
Department of Chemical Process Engineering, University of Padova, via Marzolo, 9, 35131, Padova, Italy. silvio.bicciato@unipd.it
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies