Filling the gap between biology and computer science

This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community

Sistema de Evaluación en Ingeniería del Software 2

Con la llegada del Espacio Europeo de Educación Superior (EEES), las estrategias didácticas deben cambiar para centrarse en el aprendizaje del estudiante, convirtiendo al alumno en un elemento activo dentro de su aprendizaje, incentivando su participación, de tal manera que se sienta parte activa del proceso de aprendizaje. En la asignatura de ISG2 se han incorporado un sistema de evaluación similar al ciclo de vida de un proyecto de desarrollo software, implicando a los alumnos en su propia evolución. Con este sistema, el alumno puede reflexionar acerca de sus metas, progresos, dificultades, etc. Los resultados obtenidos avalan el procedimiento llevado a cabo.Artículo revisado por pare

CarGene: Characterisation of sets of genes based on metabolic pathways analysis

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).Ministerio de Ciencia y Tecnología TIN2007-68084-C02-00Ministerio de Ciencia e Innovación PCI2006-A7-0575Junta de Andalucía P07-TIC-02611Junta de Andalucía TIC-20

Neighborhood-Based Clustering of Gene-Gene Interactions

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