Raúl Giráldez

Raúl Giráldez
Pablo de Olavide University | UPO · School of Engineering

Ph.D. Computer Science - Associate Professor, Machine Learning, Data Analytics

About

42
Publications
20,184
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
652
Citations

Publications

Publications (42)
Conference Paper
Biclustering is a powerful tool for analyzing gene expression time series, providing the ability to simultaneously explore both gene and condition dimensions. Unlike traditional clustering methods, which are limited to one dimension, biclustering uncovers local patterns of co–expression, making it particularly well–suited for the analysis of dynami...
Article
Full-text available
The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact o...
Article
Full-text available
Background Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the...
Article
Full-text available
Los métodos de predicción de mapas de contacto son un paso intermedio para la predicción de estructuras de proteínas. A pesar de los avances logrados la precisión de las predicciones continúa por debajo del umbral deseado. Una vía mediante la cual el desempeño de estos métodos puede ser elevado es realizando la predicción de las interacciones entre...
Article
Full-text available
RESUMEN Los métodos de predicción de mapas de contacto son un paso intermedio para la predicción de estructuras de proteínas. A pesar de los avances logrados la precisión de las predicciones continúa por debajo del umbral deseado. Una vía mediante la cual el desempeño de estos métodos puede ser elevado es realizando la predicción de las interaccion...
Article
Full-text available
Because of the existence of a large number of diseases of polygenic nature, a greater importance to the result of genetic interaction is imparted nowadays to the function of each gene separately. For this reason, it is very significant to study gene networks. In recent years, the problem of inferring gene association networks using various techniqu...
Conference Paper
In this work, we have extended the experimental analysis about an encoding approach for evolutionary-based algorithms proposed in [1], called probabilistic encoding. The potential of this encoding for complex problems is huge, as candidate solutions represent regions, instead of points, of the search space. We have tested in the context of gene exp...
Article
Full-text available
Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. In such cases, the development of both a suitable h...
Article
Full-text available
An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression p...
Article
Full-text available
Background Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniqu...
Article
The majority of the biclustering approaches for microarray data analysis use the Mean Squared Residue (MSR) as the main evaluation measure for guiding the heuristic. MSR has been proven to be inefficient to recognize several kind of interesting patterns for biclusters. Transposed Virtual Error (VEt) has recently been discovered to overcome MSR draw...
Conference Paper
The most widespread biclustering algorithms use the Mean Squared Residue (MSR) as measure for assessing the quality of biclusters. MSR can identify correctly shifting patterns, but fails at discovering biclusters presenting scaling patterns. Virtual Error (VE) is a measure which improves the performance of MSR in this sense, since it is effective a...
Article
Full-text available
Purpose The purpose of this paper is to present a novel control mechanism for avoiding overlapping among biclusters in expression data. Design/methodology/approach Biclustering is a technique used in analysis of microarray data. One of the most popular biclustering algorithms is introduced by Cheng and Church (2000) (Ch&Ch). Even if this heuristic...
Conference Paper
Full-text available
Resumen La mayoría de las heurísticas utilizadas para la búsqueda de biclusters en microarrays ha-cen uso del residuo cuadrático medio (MSR) como medida de evaluación de las distintas soluciones obtenidas. El uso de MSR permite obtener biclusters interesantes, sin embargo, algunos trabajos han demostrado que dicha medida no es válida para reconocer...
Conference Paper
Biclustering is a technique used in analysis of microarray data. It aims at discovering subsets of genes that presents the same tendency under a subset of experimental conditions. Various techniques have been introduced for discovering significant biclusters. One of the most popular heuristic was introduced by Cheng and Church. In the same work, a...
Article
Full-text available
Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a correct coding of the search space and an appropriate evaluation function of the potential solutions. EAs are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper,...
Conference Paper
Biclustering techniques aim at extracting significant subsets of genes and conditions from microarray gene expression data. This kind of algorithms is mainly based on two key aspects: the way in which they deal with gene similarity across the experimental conditions, that determines the quality of biclusters; and the heuristic or search strategy us...
Conference Paper
Full-text available
Many heuristics used for finding biclusters in microarray data use the mean squared residue as a way of evaluating the quality of biclusters. This has led to the discovery of interesting biclusters. Recently it has been proven that the mean squared residue may fail to identify some interesting biclusters. This motivates us to introduce a new measur...
Conference Paper
Full-text available
In recent years, the interest in extracting useful knowledge from gene expression data has experimented an enormous increase with the development of microarray technique. Biclustering is a recent technique that aims at extracting a subset of genes that show a similar behaviour for a subset conditions. It is important, therefore, to measure the qual...
Conference Paper
Traditional gene selection methods often select the top–ranked genes according to their individual discriminative power. We propose to apply feature evaluation measure broadly used in the machine learning field and not so popular in the DNA microarray field. Besides, the application of sequential gene subset selection approaches is included. In ou...
Conference Paper
Full-text available
This paper presents an approach that deals with the feature selection problem, and includes two main aspects: first, the selection is done during the evolutionary learning process, i.e., it is a dynamic approach; and second, the selection is local, i.e., the algorithm selects the best features from the best space region to learn at a given time of...
Conference Paper
Full-text available
The supervised learning methods applying evolutionary algorithms 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 carries o...
Article
Full-text available
The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources, such as evolutionary computation. Efficacy and efficiency are two critical aspects for knowledge-based techniques. The incorporation of knowledge into evo...
Article
Full-text available
Evolutionary algorithms appear as an interesting alternative to achieve minimal error rates and low numbers of rules in supervised learning tasks. In spite of the computational cost of this approach, some proposals can be applied to make the algorithm faster and more efficient. This paper describes some of these proposals, which are integrated in t...
Book
Full-text available
Prólogo La red española de Minería de Datos y Aprendizaje (subvencionada por el Ministerio de Ciencia y Tecnología mediante la acción especial TIC2002-11124-E) edita este libro como recopilación del trabajo que llevan a cabo los grupos que la integran. El objetivo de este volumen es difundir las principales líneas de investigación actuales tanto en...
Conference Paper
Full-text available
Segmentation algorithms emerge observing fluctuations of DNA sequences in alternative homogeneous domains, which are named segments [1]. The key idea is that two genes that are controlled by a single regulatory system should have similar expression patterns in any data set. In this work, we present a new approach based on Evolutionary Algorithms (E...
Article
Full-text available
Resumen Enmarcado dentro del área del aprendizaje supervisado, este trabajo describe brevemente diversos métodos algorítmicos dirigidos hacia la mejora de este tipo de técnicas para la generación de reglas de decisión (10). El principal objetivo es reducir el coste computacional asociado a los aspectos críticos de los algoritmos de aprendizaje evol...
Conference Paper
Full-text available
To select an adequate coding is one of the main problems in applications based on Evolutionary Algorithms. Many codings have been proposed to represent the search space for obtaining decision rules. A suitable representation of the individuals of the genetic population can reduce the search space, so that the learning process is accelerated by decr...
Conference Paper
Full-text available
The increasing amount of information available is encouraging the search for efficient techniques to improve the data mining methods, especially those which consume great computational resources. We present a novel structure, called EES, which helps the data mining algorithms which generate decision rules to reduce the aforementioned cost. Given th...
Article
Full-text available
Many of the supervised learning algorithms only work with spaces of dis-crete attributes. Some of the methods proposed in the bibliography focus on the dis-cretization towards the generation of decision rules. This work provides a new dis-cretization algorithm called USD (Unparametrized Supervised Discretization), which transforms the infinite spac...
Conference Paper
Full-text available
The aim of this paper is to describe a study for the obtaining, symbolically, of the separation surfaces between clusters of a labelled database. A separation surface is an equation with the form ø; (x)=0, where ø is a function of R n → R. The calculation of function ø is begun by the development of the parametric regression by means of the use of...
Conference Paper
Full-text available
Abstract In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases We introduces the theory of nearest neighbours sets in order to base the algorithm S - NN (Similar Nearest Neighbours) Traditional clustering alg...
Article
Full-text available
Resumen: Muchos de los algoritmos de aprendizaje supervisado necesitan un espacio de atributos discreto, lo que hace imprescindible la aplicación de algún método de dis- cretización que disminuya la cardinalidad d e los valores que los atributos continuos pueden tomar. Algunos de los métodos propuestos en la literatura enfocan la discreti- zación h...
Article
The aim of this paper is to describe a study for the obtaining, symbolically, of the separation surfaces between clusters of a labelled database. A separation surface is an equation with the form ø (x)=0, where ø is a function of ℜⁿ→ℜ. The calculation of function f is begun by the development of the parametric regression by means of the use of the...

Network

Cited By