Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps
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Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps. / Brameier, Markus; Wiuf, Carsten.
I: Journal of Biomedical Informatics, Bind 40, Nr. 2, 01.04.2007, s. 160-173.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps
AU - Brameier, Markus
AU - Wiuf, Carsten
PY - 2007/4/1
Y1 - 2007/4/1
N2 - We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast (Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-wide co-clustering by weighting the involved sources of information differently. Clustering quality is determined by both general and SOM-specific validation measures. Co-clustering relies on a sufficient correlation between the different datasets. We investigate in various experiments how much GO information is contained in the applied gene expression dataset and vice versa. The second major contribution is a visualization technique that applies the cluster structure of SOMs for a better biological interpretation of gene (expression) clusterings. Our GO term maps reveal functional neighborhoods between clusters forming biologically meaningful functional SOM regions. To cope with the high variety and specificity of GO terms, gene and cluster annotations are mapped to a reduced vocabulary of more general GO terms. In particular, this advances the ability of SOMs to act as gene function predictors.
AB - We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast (Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-wide co-clustering by weighting the involved sources of information differently. Clustering quality is determined by both general and SOM-specific validation measures. Co-clustering relies on a sufficient correlation between the different datasets. We investigate in various experiments how much GO information is contained in the applied gene expression dataset and vice versa. The second major contribution is a visualization technique that applies the cluster structure of SOMs for a better biological interpretation of gene (expression) clusterings. Our GO term maps reveal functional neighborhoods between clusters forming biologically meaningful functional SOM regions. To cope with the high variety and specificity of GO terms, gene and cluster annotations are mapped to a reduced vocabulary of more general GO terms. In particular, this advances the ability of SOMs to act as gene function predictors.
KW - Clustering validation
KW - Clustering visualization
KW - Co-clustering
KW - Gene expression data
KW - Gene function prediction
KW - Gene ontology
KW - Saccharomyces cerevisiae yeast
KW - Self-organizing maps
UR - http://www.scopus.com/inward/record.url?scp=33847663266&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2006.05.001
DO - 10.1016/j.jbi.2006.05.001
M3 - Journal article
C2 - 16824804
AN - SCOPUS:33847663266
VL - 40
SP - 160
EP - 173
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
SN - 1532-0464
IS - 2
ER -
ID: 203900363