Abstract
Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially
developed to be applied to binary datasets. Several approaches based on matrix factorization or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations.
We propose a novel approach to extracting biclusters from binary datasets,
BiBit. The results obtained from different experiments with synthetic data
reveal the excellent performance and the robustness of BiBit to density and
size of input data. Also, BiBit is applied to a central nervous system embryonic
tumor gene expression dataset to test the quality of the results. A
novel gene expression pre-processing methodology, based on expression level
layers, and the s ...
Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially
developed to be applied to binary datasets. Several approaches based on matrix factorization or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations.
We propose a novel approach to extracting biclusters from binary datasets,
BiBit. The results obtained from different experiments with synthetic data
reveal the excellent performance and the robustness of BiBit to density and
size of input data. Also, BiBit is applied to a central nervous system embryonic
tumor gene expression dataset to test the quality of the results. A
novel gene expression pre-processing methodology, based on expression level
layers, and the selective search performed by BiBit, based on a very fast
bit-pattern processing technique, provide very satisfactory results in quality
and computational cost. The power of biclustering in finding genes involved
simultaneously in different cancer processes is also shown. Finally, a comparison
with Bimax, one of the most cited binary biclustering algorithms,
shows that BiBit is faster while providing essentially the same results.
Besides, in this work, we introduce a software tool, named CarGene (Characterization
of Genes), that helps scientists to validate sets of genes using
biological knowledge. The integration of huge databases with searching techniques
in order to automatically validate results from different sources is
a key factor in bioinformatics. Several tools have been developed for analysing
gene¿enrichment in terms. Most of them are Gene Ontology-based tools,
i.e., these analyse gene-enrichment in GO annotations. CarGene uses metabolic
pathways stored in the Kyoto Encyclopedia of Genes and Genomes
(Kegg) and provides a friendly graphical environment to analyse and compare
results generated by different clustering and/or biclustering techniques.
CarGene is based on the degree of coherence of genes in (bi)clusters with
respect to metabolic pathways of organisms stored in Kegg, and provides an
estimate of obtaining results by chance, including two statistical corrections
(Bonferroni, andWestfall and Young). One of the most important features
of CarGene is the possibility of simultaneously comparing and statistically
analysing the information about many groups of genes in both visual and
textual manner. Furthermore, it includes its own web browser to explore in
detail the information extracted from Kegg.
Descripción
Programa de Doctorado en Ingeniería y Tecnología del Software
