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Mark Irwin

dblp:41/4188 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 92% Computational science and engineering · 8%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › cancer genomics › cancer classification
cancer class discovery		 0.012004
Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach · Bioinform. 2004
Bioinformatics and computational biology › cancer genomics
cancer classification		 0.012004
Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach · Bioinform. 2004
Bioinformatics and computational biology
cancer genomics		 0.012004
Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach · Bioinform. 2004
Bioinformatics and computational biology
gene expression analysis		 0.012004
Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach · Bioinform. 2004
Computational science and engineering › latent variable model
mixture model		 0.012004
Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach · Bioinform. 2004

Methods — techniques the papers use, named apart from their topics

finite mixture model · 0.0cross-validation · 0.0
YearPublicationVenuePosition
2004 Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach
abstract
MOTIVATION: The DNA microarray technology has been increasingly used in cancer research. In the literature, discovery of putative classes and classification to known classes based on gene expression data have been largely treated as separate problems. This paper offers a unified approach to class discovery and classification, which we believe is more appropriate, and has greater applicability, in practical situations. RESULTS: We model the gene expression profile of a tumor sample as from a finite mixture distribution, with each component characterizing the gene expression levels in a class. The proposed method was applied to a leukemia dataset, and good results are obtained. With appropriate choices of genes and preprocessing method, the number of leukemia types and subtypes is correctly inferred, and all the tumor samples are correctly classified into their respective type/subtype. Further evaluation of the method was carried out on other variants of the leukemia data and a colon dataset.
Roxana Alexandridis, Shili Lin, Mark Irwin
 Bioinform.3