Gene Ontology Friendly Biclustering of Expression Profiles
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
CLUGO: A Clustering Algorithm for Automated Functional Annotations Based on Gene Ontology
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
An integrative approach for biological data mining and visualisation
International Journal of Data Mining and Bioinformatics
Gene Ontology Assisted Exploratory Microarray Clustering and Its Application to Cancer
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology
Pattern Recognition Letters
Interpreting microarray experiments via co-expressed gene groups analysis (CGGA)
DS'06 Proceedings of the 9th international conference on Discovery Science
Incorporating biological domain knowledge into cluster validity assessment
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Spectral clustering gene ontology terms to group genes by function
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Kernel: based visualisation of genes with the gene ontology
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
GOtoGene: a method for determining the functional similarity among gene products
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Functional visualisation of genes using singular value decomposition
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Motivation: With the advent of DNA microarray technologies, the parallel quantification of genome-wide transcriptions has been a great opportunity to systematically understand the complicated biological phenomena. Amidst the enthusiastic investigations into the intricate gene expression data, clustering methods have been the useful tools to uncover the meaningful patterns hidden in those data. The mathematical techniques, however, entirely based on the numerical expression data, do not show biologically relevant information on the clustering results. Results: We present a novel methodology for biological interpretation of gene clusters. Our graph theoretic algorithm extracts common biological attributes of the genes within a cluster or a group of interest through the modified structure of gene ontology (GO) called GO tree. After genes are annotated with GO terms, the hierarchical nature of GO terms is used to find the representative biological meanings of the gene clusters. In addition, the biological significance of gene clusters can be assessed quantitatively by defining a distance function on the GO tree. Our approach has a complementary meaning to many statistical clustering techniques; we can see clustering problems from a different viewpoint by use of biological ontology. We applied this algorithm to the well-known data set and successfully obtained the biological features of the gene clusters with the quantitative biological assessment of clustering quality through GO Biological Process. Availability: The software is available on request from the authors.