Paper Info
Title
The probabilities mixture model for clustering flow-cytometric data: an application to gating lymphocytes in peripheral blood
Abstract
Data clustering is a major data mining technique and has been shown to be useful in a wide variety of domains, including medical and biological statistical data analysis. A non trivial application of cluster analysis occurs in the identification of different subpopulations of particles in large-sized heterogeneous flow-cytometric data. Mixture-model based clustering has been several times applied in the past to medical and biological data analysis; to our knowledge, however, non of these applications was involved with flow-cytometric data. We claim, that utilizing the probabilities mixture model offers several advantages compared to other proposed flow-cytometric data clustering approaches. We apply this model in order to gate lymphocytes in peripheral blood, which is a necessary first-step procedure when dealing with various hematological diseases diagnoses, such as lymphocytic leukemias and lymphoma.
Year
DOI
Venue
2006
10.1007/11946465_14
ISBMDA
Keywords
Field
DocType
major data mining technique,non trivial application,peripheral blood,gating lymphocyte,flow-cytometric data,large-sized heterogeneous flow-cytometric data,proposed flow-cytometric data,data clustering,biological statistical data analysis,probabilities mixture model,biological data analysis,cluster analysis,mixture model,biological data,data mining
Biological data,Data mining,Gating,Computer science,Data cluster,Bayesian network,Hematological Diseases,Cluster analysis,Mixture model,Medical diagnosis
Conference
Volume
ISSN
ISBN
4345
0302-9743
3-540-68063-2
Citations 
PageRank 
References 
4
0.68
4
Authors
6
Name
Order
Citations
PageRank
John Lakoumentas1443.15
John Drakos2432.45
Marina Karakantza3432.45
Nicolaos Zoumbos440.68
George Nikiforidis522521.70
George Sakellaropoulos6132.80