NEPOMUCENO CHAMORRO , Isabel De Los Ángeles

Membrane Computing is a recent area of Natural Computing, a topic where much work has been done but still much remains to be done. There are some applica tions which have been developed in imperative languages, like C++, or in declaratives languages, as Prolog, working in the framework of P systems. In this paper, a software tool (called SimCM,...

In this paper, a software tool (called SimCM, from Spanish Sim- ulador de Computaci¶on con Membranas) for handling P systems is presented. The program can simulate basic transition P Systems where dissolution of membranes and priority rules are allowed. This is a ¯rst step to cross the border between simulations and distributed implementations...

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...

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The Regression Network plugin for Cytoscape (RegNetC) implements the RegNet algorithm for the inference of transcriptional association network from gene expression profiles. This algorithm is a model tree-based method to detect the relationship between each gene and the remaining genes simultaneously instead of analyzing individually each pair...

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...

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...

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of...

Predictive monitoring is a key activity in some Process Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether...

Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as...

Background: Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among...

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The interest in this technique has grown exponentially in recent years and the difficulties in analyzing data from such experiments, which are characterized by the high number of genes to be analyzed in relation to the low number...

The supervised learning methods applying evolutionary al gorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process...

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem...

The importance of electricity in people's daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in...

The olive oil assessment involves the use of a standardized sensory analysis according to the "panel test" method. However, there is an important interest to design novel strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry (MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment for...

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to new protection data laws that are emerging. Generative Adversarial Networks (GANs) and its variants have attracted many researchers in their research work due to its elegant theoretical basis and its great performance in the generation...