Juan Pablo Hernández Ramos

An Agent-Based Clustering Approach for Gene Selection in Gene Expression Microarray

Gene selection is a major research area in microarray analysis, which seeks to discover differentially expressed genes for a particular target annotation. Such genes also often called informative genes are able to differentiate tissue samples belonging to different classes of the studied disease. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This research proposes a gene selection approach by means of a clustering-based multi-agent system. This proposal manages different filter methods and gene clustering through coordinated agents to discover informative gene subsets. To assess the reliability of our approach, we have used four important and public gene expression datasets, two Lung cancer datasets, Colon and Leukemia cancer dataset. The achieved results have been validated through cluster validity measures, visual analytics, a classifier and compared with other gene selection methods, proving the reliability of our proposal.

Multi-agent System for Obtaining Relevant Genes in Expression Analysis between Young and Older Women with Triple Negative Breast Cancer.

Triple negative breast cancer is an aggressive form of breast cancer. Despite treatment with chemotherapy, relapses are frequent and response to these treatments is not the same in younger women as in older women. Therefore, the identification of genes that cause this difference is required. The identification of therapeutic targets is one of the sought after goals to develop new drugs. Within the range of different hybridization techniques, the developed system uses expression array analysis to measure the expression of the signal levels of thousands of genes in a given sample. Probesets of Gene 1.0 ST GeneChip arrays provide categorical genome transcript coverage, providing a measurement of the expression level of the sample. This paper proposes a multi-agent system to manage information of expression arrays, with the goal of providing an intuitive system that is also extensible to analyze and interpret the results. The roles of agent integrate different types of techniques, statistical and data mining methods that select a set of genes, searching techniques that find pathways in which such genes participate, and an information extraction procedure that applies a CBR system to check if these genes are involved in the disease.

A Clustering-Based Method for Gene Selection to Classify Tissue Samples in Lung Cancer

This paper proposes a gene selection approach based on clustering of DNA-microarray data. The proposal has been aimed at finding a boundary gene subset coming from gene groupings imposed by a clustering method applied to the case study: gene expression data in lung cancer. Thus, we assume that such a found gene subset represents informative genes, which can be used to train a classifier by learning tumor tissue samples. To do this, we compare the results of several methods of hierarchical clustering to select the best one and then choose the most suitable clustering based on visualization techniques. The latter is used to compute its boundary genes. The results achieved from the case study have shown the reliability of this approach.

Sujetos, objetos, decisiones y evasiones - el proceso de aprobación de la Ley de Comunicación en Ecuador

The new regulatory processes in Communication in several Latin American countries are linked to a political reform process called “Latin American progressive left.” The latest country to join this reformist trend is Ecuador, where after four years of debate has reached an agreement about this topic. This paper tries to understand the policymaking process. Moreover, aims to know how the debate developed on the new Communication regulation in Ecuador. Through documentary analysis and following the study methodology of policy net works (SChArPF, 1993, PArSonS, 2007) the paper aims to know how advanced the policymaking discussion regarding the interests of the different policynetworks, showing the evolution of main issues between the different projects and the final document, and the main themes of interest and discussion during this process, finally establishing what issues were modified and which were more complex to reverse.

A Genetic Programming Approach Applied to Feature Selection from Medical Data.

Genetic programming represents a flexible and powerful evolutionary technique in machine learning. The use of genetic programming for rule induction has generated interesting results in classification problems. This paper proposes an evolutionary approach for logical rule induction, which is applied to clinical data. Since logical rules disclose knowledge from the analyzed data, we use such a knowledge to filter features from the target dataset. The results reached by the used dataset have been very promising when used in classification tasks and compared with other methods.

An Ensemble Framework Coping with Instability in the Gene Selection Process

This paper proposes an ensemble framework for gene selection, which is aimed at addressing instability problems presented in the gene filtering task. The complex process of gene selection from gene expression data faces different instability problems from the informative gene subsets found by different filter methods. This makes the identification of significant genes by the experts difficult. The instability of results can come from filter methods, gene classifier methods, different datasets of the same disease and multiple valid groups of biomarkers. Even though there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This work proposes a framework involving five stages of gene filtering to discover biomarkers for diagnosis and classification tasks. This framework performs a process of stable feature selection, facing the problems above and, thus, providing a more suitable and reliable solution for clinical and research purposes. Our proposal involves a process of multistage gene filtering, in which several ensemble strategies for gene selection were added in such a way that different classifiers simultaneously assess gene subsets to face instability. Firstly, we apply an ensemble of recent gene selection methods to obtain diversity in the genes found (stability according to filter methods). Next, we apply an ensemble of known classifiers to filter genes relevant to all classifiers at a time (stability according to classification methods). The achieved results were evaluated in two different datasets of the same disease (pancreatic ductal adenocarcinoma), in search of stability according to the disease, for which promising results were achieved.

An Ensemble Approach for Gene Selection in Gene Expression Data

Feature/Gene selection is a major research area in the study of gene expression data, generally dealing with classification tasks of diseases or subtype of diseases and identification of biomarkers related to a type of disease. In such a context, this paper proposes an ensemble approach of gene selection for classification tasks from gene expression datasets. This proposal provides a four-staged approach of gene filtering. Each stage performs a different gene filtering task, such as: data processing, noise removing, gene selection ensemble and application of wrapper methods to reach the end result, a small subset of informative genes. Our proposal has been assessed on two different datasets of the same disease (Pancreatic ductal adenocarcinoma) for which, good results have been achieved in comparison with other gene selection methods. Hence, the proposed strategy has proven its reliability with respect to other approaches.

Evaluación de Competencias Informacionales con el Instrumento IL-HUMASS: Escalamiento Multidimensional

espanolEn este trabajo se muestra el comportamiento multivariante de la escala IL-HUMASS, compuesta de 26 items de escala tipo Likert y 4 dimensiones teoricas, para la evaluacion del nivel autopercibido encompetencias informacionales. Se aplica la escala a una muestra de 402 sujetos, futuros profesores de educacion secundaria en Espana. La tecnica de escalamiento multidimensional mediante ALSCAL, muestra dos dimensiones de maxima explicacion que explican el 95.9% de la varianza de la matriz. Las dimensiones tienen que ver con la familiaridad con el desempeno propuesto en el item en primer lugar, y con el componente instrumental o cognitivo del item, en segundo lugar. Se concluye relacionando estas dos dimensiones con las dimensiones teoricas de la escala inicial. En las conclusiones se senala la importancia de tener en cuenta las dos dimensiones obtenidas, ademas de las dimensiones teoricas senaladas, a la hora de disenar escalas relacionadas con la evaluacion de competencias informacionales EnglishThe multivariate characteristics of the IL-HUMASS scale are studied. This scale is formed by 26 Likert scale items and 4 dimensions and evaluate the information literacy self-perceived level of students. A sample of 402 subjects, all of them Spanish future teachers from secondary education, is obtained. It is implemented a multidimensional scaling technique by ALSCAL based on the distance matrix from the data. The results detected two empirical dimensions, which accounted for 95.9% of the variance. The first dimension is related to the level of familiarity with the content of the item andthe second dimension is related to cognitive or instrumental aspect of the item. In the conclusions, two dimensions obtained are connected with the original scale, in relation to the four theoretical dimensions. On the other hand, the conclusions highlight the importance of considering the two dimensions obtained, in addition to the classical dimensions, in the design of assessment scales of the construct information literacy

Subjects, objects, decisions and escapes - the approval process for Communication Act in Ecuador

The new regulatory processes in Communication in several Latin American countries are linked to a political reform process called “Latin American progressive left.” The latest country to join this reformist trend is Ecuador, where after four years of debate has reached an agreement about this topic. This paper tries to understand the policymaking process. Moreover, aims to know how the debate developed on the new Communication regulation in Ecuador. Through documentary analysis and following the study methodology of policy net works (SChArPF, 1993, PArSonS, 2007) the paper aims to know how advanced the policymaking discussion regarding the interests of the different policynetworks, showing the evolution of main issues between the different projects and the final document, and the main themes of interest and discussion during this process, finally establishing what issues were modified and which were more complex to reverse.

Sujeitos, objetos, decisões e evasões – o processo de aprovação da Lei de Comunicação no Equador

The new regulatory processes in Communication in several Latin American countries are linked to a political reform process called “Latin American progressive left.” The latest country to join this reformist trend is Ecuador, where after four years of debate has reached an agreement about this topic. This paper tries to understand the policymaking process. Moreover, aims to know how the debate developed on the new Communication regulation in Ecuador. Through documentary analysis and following the study methodology of policy net works (SChArPF, 1993, PArSonS, 2007) the paper aims to know how advanced the policymaking discussion regarding the interests of the different policynetworks, showing the evolution of main issues between the different projects and the final document, and the main themes of interest and discussion during this process, finally establishing what issues were modified and which were more complex to reverse.