Result: Pattern recognition in expression data

Title:
Pattern recognition in expression data
Authors:
KA YEE YEUNG, BUMGARNER, Roger E
Source:
Recent developments in nucleic acids research Vol. 1 - 2004 Part IIRecent developments in nucleic acids research. :333-354
Publisher Information:
Trivandrum: Transworld Research Network, 2004.
Publication Year:
2004
Physical Description:
print, 78 ref
Original Material:
INIST-CNRS
Subject Terms:
Biochemistry, molecular biology, biophysics, Biochimie, biologie moléculaire, biophysique, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Biologie moleculaire et cellulaire, Molecular and cellular biology, Méthodes diverses, Diverse techniques, Analyse amas, Cluster analysis, Analisis cluster, Article synthèse, Review, Artículo síntesis, Classification, Clasificación, Expression génique, Gene expression, Expresión genética
Document Type:
Book Book Chapter
File Description:
text
Language:
English
Author Affiliations:
Department of Microbiology, Box 358070, University of Washington, Seattle WA 98195, United States
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17751690
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Molecular and cell biology
Accession Number:
edscal.17751690
Database:
PASCAL Archive

Further Information

Microarrays allow the measurement of mRNA expression levels on a genome-wide scale. Many methods and tools have been developed to extract information from large microarray datasets. Among these, clustering and classification are the most commonly used analytical techniques. Cluster analysis is an unsupervised technique in which similar objects are assigned to the same groups (or clusters). Classification is a supervised technique that requires external information of the dataset (such as tissue types, disease states, etc). In classification, labels such as types of samples) are assigned to objects (typically experiments). In this review article, we will discuss the applications of clustering and classification algorithms and survey popular clustering and classification algorithms for the analysis of gene expression data. We will also discuss the limitations of these methods and approaches to circumvent these limitations.