IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) is a quarterly journal that publishes archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology. Read more about TCBB
From the January/March 2010 Issue...
Data-Fusion in Clustering Microarray Data: Balancing Discovery and Interpretability
By Rafal Kustra and Adam Zagdański
While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.
Special Issue on BioCreative II.5 (Critical Assessment for Information Extraction in Biology)
TCBB is a joint publication of the IEEE Computer Society, Association for Computing Machinery, IEEE Computational Intelligence Society, and the IEEE Engineering in Medicine and Biology Society.
While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.
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