NEPOMUCENO CHAMORRO , Juan Antonio

Los datos de expresión génica, y su particular naturaleza e importancia, motivan no sólo el desarrollo de nuevas técnicas sino la formulación de nuevos problemas como el problema del biclustering. El biclustering es una técnica de aprendizaje no supervisado que agrupa tanto genes como condiciones. Este doble agrupamiento lo diferencia del...

In this paper we present a software tool to represent P systems modelling signalling networks of biochemical reactions using SBML (Systems Biology Markup Language), a machine-readable format for describing qualitative and quantitative models of biochemical networks. CLIPS (C Language Integrated Production System), a tool which provides a...

In this paper, a new algorithm Database Reduction Simulta neously by Ordered Projections (RESOP) is introduced. This algorithm reduces databases in two directions: editing examples and feature se lection simultaneously. Ordered projections techniques have been used to design RESOP taking advantage of symmetrical ideas for two dif ferent task....

In this paper we study a measure, named weakness of an example, which allows us to establish the importance of an example to find representative patterns for the data set editing problem. Our ap proach consists in reducing the database size without losing information, using algorithm patterns by ordered projections. The idea is to relax...

Biclustering is an unsupervised machine learning technique that simultaneously clusters genes and conditions in gene expression data. Gene Ontology (GO) is usually used in this context to validate the biological relevance of the results. However, although the integration of biological information from different sources is one of the...

El marco del Espacio Europeo de Educación Supe rior (EEES) y la implantación del Sistema de Crédito Europeo (ECTS) han motivado un cambio en el mo delo educativo. De un modelo basado en clases ma gistrales se ha evolucionado hacia un modelo basado en el aprendizaje por competencias del alumnado. Esto implica un cambio en el sistema de...

Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect to the same experimental condition and not due to the same regulatory regime. For this reason,...

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a...

En este trabajo se propone el uso de conocimiento a priori como heurística en métodos de inferencia de redes de genes a partir de datos de expresión obtenidos con tecnología de Microarray. Utilizamos Gene Ontology [15] como fuente de conocimiento a priori. Este repositorio se nutre de la información de anotaciones de relaciones en el material...

Background and objective: The major difficulty of the analysis of the input gene expression data in a microarray-based approach for an automated diagnosis of can cer is the large number of genes (high dimensionality) with many irrelevant genes (noise) compared to the very small number of samples. This research study tackles the dimensionality...

Traditional computational techniques are recently being improved with the use of prior biological knowledge from open access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we...

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some...

P systems can be classi ed in two main groups: P systems with the membrane structure described by a tree, and tissue P systems with the membranes placed in the nodes of an arbitrary graph. NP-complete problems have been solved in linear time by trading space for time in the framework of recognizing tissue P systems with cell division. The...

Scatter Search is a population-based metaheuristic that emphasizes systematic processes against random proce dures. A local search procedure is added to a Scatter Search for Biclustering in order to improve the quality of biclusters. This local search constitutes the existing Improvement Method in most of Scatter Search schemes which...

Centrality measures are used in network analysis to identify the relevant elements in a network. Recently, several centrality measures based on coalitional game theory have been successfully applied to dif ferent kinds of biological networks, such as brain networks, gene networks, and metabolic networks. We propose an approach, using...

This paper presents a scatter search approach based on linear correlations among genes to find biclusters, which include both shifting and scaling patterns and negatively correlated patterns contrarily to most of correlation-based algorithms published in the literature. The methodology established here for compari son is based on a priori...

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high–quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from...

The identification of regulatory modules is one of the most important tasks in order to discover disease markers. This paper presents a methodology to infer coexpression networks based on local patterns in gene expression data matrix. In the proposed algorithm two steps can clearly be differentiated. Firstly, a Biclustering procedure that uses...

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Scatter Search is an evolutionary method that combines ex isting solutions to create new offspring as the well–known genetic algo rithms. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. However, biclusters with certain patterns are more interesting from a biological point of view....

We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex...

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is pre sented. A general scheme of Scatter Search has been used to obtain high–quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Moreover, in the own...

A new measure to evaluate the quality of a bicluster is proposed in this paper. This measure is based on correlations among genes. Moreover, a new evolutionary metaheuristic based on Scatter Search, which uses this measure as the fitness function, is presented to obtain biclusters that contain groups de highly-correlated genes. Later, an...

Microarray techniques have motivated the develop of differ ent methods to extract useful information from a biological point of view. Biclustering algorithms obtain a set of genes with the same behaviour over a group of experimental conditions from gene expression data. In order to evaluate the quality of a bicluster, it is useful to identify...

The results of feature selection methods have a great influence on the success of data mining processes, especially when the data sets have high dimensionality. In order to find the optimal result from feature selection methods, we should check each possible subset of features to obtain the precision on classification, i.e., an exhaustive...