Características y predictores de la violencia severa contra la pareja.

La violencia severa perpetrada por el hombre contra la mujer en la relación de pareja (VCP-S), que es aquella que incluye el feminicidio consumado o intentado, las agresiones con armas u objetos contundentes y otrasmodalidades de agresiones potencialmente peligrosas para la vida de la mujer, es un grave problema de saludpública que requiere medidas urgentes para prevenirla. En España, entre el año 2003 y el 2020, fueron asesinadas1,078 mujeres, lo que supone una media aproximada de 60 feminicidios por año, mientras que, según la últimamacroencuesta, otras agresiones físicas severas, como darle patadas, arrastrarla, propinarle una paliza, intentarasfixiarla, quemarla o agredirla con armas o sustancias peligrosas, ocurrió aproximadamente en un 5% de todos loscasos. Las consecuencias de esta clase de violencia para las mujeres que la sufren son alarmantes, pues, ademásde las que son asesinadas, aquellas que son sometidas a formas extremas de violencia presentan tasas más altasde depresión, estrés postraumático, abuso de sustancias, ansiedad, problemas de salud física y suicidio. Pese aello, la VCP-S no afecta exclusivamente a las mujeres, ya que los estudios enumeran numerosas consecuencias perjudiciales para la salud de los hijos que han perdido a su madre como consecuencia de un feminicidio o que son testigos del maltrato.Existe un amplio consenso político, social y científico en cuanto a la necesidad de llevar a cabo acciones urgentes para proteger a las víctimas que se encuentran en riesgo de ser asesinadas. Sin embargo, para ello es preciso quelas medidas que se establezcan se basen en la evidencia científica, lo cual lleva a plantearse qué característicasson propias de la VCP-S, que tipos de agresores la ejercen, bajo qué circunstancias y qué factores devulnerabilidad de las víctimas las exponen a un mayor riesgo de padecer una agresión letal. A pesar de que existenestudios que han aportado un conocimiento valioso en este ámbito, todavía existen numerosas carencias y problemas metodológicos que limitan el alcance de los resultados obtenidos. Intentando superar algunas de estas limitaciones, esta tesis, que está compuesta por tres estudios, pretende avanzar en el conocimiento de los principales determinantes de esta violencia a partir del análisis de una muestra amplia y representativa de los formularios (Questionaris Policials de Valoració del Risc [QPVR]) que utilizó la policía catalana para evaluar elriesgo de VCP-S con motivo de la interposición de denuncias por violencia contra la pareja (en adelante, VCP) en Catalunya en los años 2016 y 2017.2020-2

Gene Ranking from Microarray Data for Cancer Classification : A Machine Learning Approach

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 our study, we propose some well-known criteria (filters and wrappers) to rank attributes, and a greedy search procedure combined with three subset evaluation measures. Two completely different machine learning classifiers are applied to perform the class prediction. The comparison is performed on two well–known DNA microarray data sets. We notice that most of the top-ranked genes appear in the list of relevant–informative genes detected by previous studies over these data sets.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004–00159Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-06689C030

Biclustering on expression data: A review

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 heuristics and a good measure for guiding the search are essential for discovering interesting biclusters in an expression matrix. Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. There exists a diverse set of biclustering tools that follow different strategies and algorithmic concepts which guide the search towards meaningful results. In this paper we present a extensive survey of biclustering approaches, classifying them into two categories according to whether or not use evaluation metrics within the search method: biclustering algorithms based on evaluation measures and non metric-based biclustering algorithms. In both cases, they have been classified according to the type of meta-heuristics which they are based on.Ministerio de Economía y Competitividad TIN2011-2895

Evolutionary Biclustering based on Expression Patterns

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 drawbacks, being able to recognize shifting and/or scaling patterns. In this work we propose a parallel evolutionary biclustering algorithm which uses VEt as the main part of the fitness function, which has been designed using the volume and overlapping as other objectives to optimize. The resulting algorithm has been tested on both synthetic and benchmark real data producing satisfactory results. These results has been compared to those of the most popular biclustering algorithm developed by Cheng and Church and based in the use of MSR.Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Shifting Patterns Discovery in Microarrays with Evolutionary Algorithms

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 quality of a bicluster, and a way to do that would be checking if each data submatrix follows a specific trend, represented by a pattern. In this work, we present an evolutionary algorithm for finding significant shifting patterns which depict the general behaviour within each bicluster. The empirical results we have obtained confirm the quality of our proposal, obtaining very accurate solutions for the biclusters used.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-00159Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-06689C030

Measuring the Quality of Shifting and Scaling Patterns in Biclusters

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 at recognizing biclusters containing shifting patters or scaling patterns as quality biclusters. However, VE presents some drawbacks when the biclusters present both kind of patterns simultaneously. In this paper, we propose a improvement of VE that can be integrated in any heuristic to discover biclusters with shifting and scaling patterns simultaneously.Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Configurable Pattern-based Evolutionary Biclustering of Gene Expression Data

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 techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. RESULTS: Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). CONCLUSIONS: We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology

Separation Surfaces through Genetic Programming

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 the Genetic Programming. The symbolic regression consists in approximating an unknown function’s equation, through knowledge of certain points’ coordinates and the value that a function reaches with the same ones. This possibility was propose in [Koza92a] and its advantage in front of the classic statistical regressions is that it is not necessary previously to know the form the function. Once this surface is found, a classifier for the database could be obtained. The technique has been applied to different examples and the results have been very satisfactory

Evolutionary Search of Biclusters by Minimal Intrafluctuation

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 used for exploring the search space. A measure that is often adopted for establishing the quality of biclusters is the mean squared residue. This measure has been successfully used in many approaches. However, it has been recently proven that the mean squared residue fails to recognize some kind of biclusters as quality biclusters, mainly due to the difficulty of detecting scaling patterns in data. In this work, we propose a novel measure for trying to overcome this drawback. This measure is based on the area between two curves. Such curves are built from the maximum and minimum standardized expression values exhibited for each experimental condition. In order to test the proposed measure, we have incorporated it into a multiobjective evolutionary algorithm. Experimental results confirm the effectiveness of our approach. The combination of the measure we propose with the mean squared residue yields results that would not have been obtained if only the mean squared residue had been used.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2004-0015