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This article proposes, and justifies, the use of the Document-oriented databases as a flexible, easy to use, and powerful digital tool in the field of historical research. First, the reasons that have made relational databases the predominant instrument among historians are studied, while detailing the problems involved in their use. Next, the way in which historians have tried to face these problems by using other digital tools is explained, as well as the limitations that such use entails. Through a case study—that of European aristocratic networks in early modern times—it is shown, however, that Document-oriented databases, present notable advantages and have greater explanatory power for the historian’s work. Thanks to their flexibility, they are better adapted to the often-unpredictable nature of historical sources without diminishing their ease of use or their analytical potential.

The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them are Gene Ontology-based tools. We developed a Kyoto Encyclopedia of Genes and Genomes (Kegg)-based tool that provides a friendly graphical environment for analysing gene-enrichment. The tool integrates two statistical corrections and simultaneously analysing the information about many groups of genes in both visual and textual manner. We tested the usefulness of our approach on a previous analysis (Huttenshower et al.). Furthermore, our tool is freely available (http://www.upo.es/eps/bigs/cargene.html).

Microarray experiments help researches to construct the str ucture of gene regulatory networks, i.e., networks representing relation ships among different genes. Filter and knowledge extraction processes are necessary in order to handle the huge amount of data produced by microarray technologies. We propose regression trees techniques as a method to identify gene networks. Regression trees are a very use ful technique to estimate the numerical values for the target outputs. They are very often more precise than linear regression models because they can adjust different linear regressions to separate areas of the search space. In our approach, we generate a single regression tree for each genes from a set of genes, taking as input the remaining genes, to finally build a graph from all the relationships among output and input genes. In this paper, we will simplify the approach by setting an only seed, the gene ARN1, and building the graph around it. The final model might gives some clues to understand the dynamics, the regulation or the topology of the gene network from one (or several) seeds, since it gathers rele vant genes with accurate connections. The performance of our approach is experimentally tested on the yeast Saccharomyces cerevisiae dataset (Rosetta compendium).

El análisis de datos de expresión genética es una de las tareas fundamentales dentro de la Bioinformática. Para llevar a cabo este estudio se hace necesaria la aplicación de técnicas de Minería de Datos. Las técnicas de Clustering han probado ser de gran utilidad a la hora de descubrir grupos de genes que intervienen en una misma función celular o que están regulados de la misma manera. Recientemente, el Biclustering ha sido propuesto como método para descubrir patrones de comportamiento especí co en los que el valor de expresión de un subgrupo de genes evoluciona de la misma forma a lo largo de un subgrupo de condiciones de laboratorio. En este artículo se revisan las distintas técnicas usadas en el análisis de datos de expresión genética, estudiándose en profundidad los métodos basados en Biclustering, además de discutir los diferentes métodos de validación para evaluar el modelo generado por las distintas propuestas.

n this work, we propose a new greedy clustering algorithm to identify groups of related genes. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. Our approach presents some interesting properties. For instance, the user can specify how the range of each gene is going to be segmented (labels). Some of these will mean expressed or inhibited (depending on the gradation). From all the label combinations a function transforms each pair of labels into another one, that identifies the type of interaction. From these pairs of genes and their interactions we build clusters in a greedy, iterative fashion, as two pairs of genes will be similar if they have the same amount of relevant interactions. Initial two–genes clusters grow iteratively based on their neighborhood until the set of clusters does not change. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene–gene mapping function and filtering function, and provides much flexibility to obtain clusters based on the level of precision needed. The performance of our approach is experimentally tested on the yeast dataset. The final number of clusters is low and genes within show a significant level of cohesion, as it is shown graphically in the experiments.

E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in which every employee spends about 90 minutes a day in e–mail tasks such as filing and deleting. This paper deals with the generation of clusters of relevant words from E–mail texts. Our approach consists of the application of text mining techniques and, later, data mining techniques, to obtain related concepts extracted from sent and received messages. We have developed a new clustering algorithm based on neighborhood, which takes into account similarity values among words obtained in the text mining phase. The potential of these applications is enormous and only a few companies, mainly large organizations, have invested in this project so far, taking advantage of employees’s knowledge in future decisions.