TriGen: A genetic algorithm to mine triclusters in temporal gene expression data

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Ministerio de Ciencia y Tecnología TIN2009-13950Junta de Andalucía TIC-752

A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula

A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the available seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen’s self-organizing maps.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12-TIC-1728Universidad Pablo de Olavide APPB81309

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

<p>Abstract</p> <p>Background</p> <p>Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.</p> <p>Results</p> <p>We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (<it>Plasmodium chabaudi</it>), systemic acquired resistance in <it>Arabidopsis thaliana</it>, similarities and differences between inner and outer cotyledon in <it>Brassica napus </it>during seed development, and to <it>Brassica napus </it>whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.</p> <p>Conclusions</p> <p>Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.</p

Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies

Urinary bladder cancer (UBC) is the second most common urogenital solid tumor and the eleventh in the rank among all types of solid tumors. Although several oncogenes and tumor suppressors are known to be implicated in the disease, the list of candidate prognostic markers has recently expanded, as a result of the power of new high-throughput methodologies. The prognosis and therapy of UBC have progressed greatly during the last years. However, a majority of the different tumor subtypes still relapses, manifesting poor prognosis. Here, we identified gene expression patterns being common across different histological phenotypes of UBC. Such an approach could be useful in the discovery of prognostic and therapeutic targets able to be applied in the majority of the tumor’s subtypes