Gestión de volúmenes masivos de datos genéticos y análisis de la influencia de su interacción en el desarrollo de cáncer

RESUMEN: Los modelos o patrones en las asociaciones entre una variante genética (o una interacción de estas) y una enfermedad, a pesar de la información que propor-cionan, se han ignorado en casi todos los estudios de asociación del genoma completo. Aunque no todas las variantes genéticas, ni mucho menos todas sus interacciones, presentan un modelo en su relación con la enfermedad, la hipó-tesis de partida de esta tesis doctoral era que no son tan reducidas en número como parece, por lo que su estudio podía dar lugar a la generación de hipótesis biológicas susceptibles de ser comprobadas experimentalmente. Para demos-trarlo, (i) se desarrolló un marco de trabajo que permitió evaluar y comparar los niveles de adecuación e incertidumbre de distintos patrones a volúmenes masivos de variables que analizar simultáneamente; (ii) se diseñó e implemen-tó una prueba estadística en el marco de trabajo anterior que permitió decidir qué modelo genético le correspondía a variantes genéticas e interacciones de estas; y (iii) se confeccionó un protocolo de construcción de redes de interac-ciones con que se analizaron los datos del estudio MCC-Spain. Las asociacio-nes encontradas han podido refrendarse con descubrimientos científicos de los últimos 5 años, lo que pone de manifiesto tanto la viabilidad del método como su potencial para revelar información oculta en las redes de interaccio-nes de variantes genéticas que conducen a la aparición de enfermedades co-munes.ABSTRACT: The presence of a model or a pattern in the association between a genetic vari-ant (or a variant–variant interaction) and a disease, despite the fact that it pro-vides a wealth of information, has been ignored by genome-wide association studies. Although these models do not underlie every variant–disease (let alone every interaction–disease) association, the working hypothesis of this doctoral dissertation, contrary to what intuition would indicate, was that they are abundant, which might give rise to biological hypotheses to be tested ex-perimentally. In order to confirm it, (i) we developed a framework that al-lowed us to evaluate and compare the patterns in massive datasets with vari-ables to be analyzed simultaneously; (ii) we designed and implemented a sta-tistical test that allowed us to decide which genetic model corresponded with each genetic variant and interaction; and (iii) we composed a protocol for gen-erating interaction networks and analyzing the data from the MCC-Spain study. The associations found are supported by scientific discoveries in the past 5 years, which demonstrates both the viability of the method and its abil-ity to reveal the information hidden in variant–variant interaction networks leading to the development of common diseases

Activity in the field of Human-Computer Interaction of a work team integrated in the MCFLAI research group

Se presenta la actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de CantabriaThe activity in the field of Human-Computer Interaction of a work team integrated in the research group MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) of the University of Cantabria is presented

Risk model for prostate cancer using environmental and genetic factors in the spanish multi-case-control (MCC) study

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model

Detection of Overlapping Communities in Directed and Weighted Social Networks

RESUMEN:Con la reciente popularidad de los servicios de redes sociales, como Facebook o Twitter, la detección de comunidades se ha convertido en un problema de un interés considerable. A pesar de que se han propuesto decenas de algoritmos que permiten detectar comunidades en redes sociales, solo un reducido subconjunto de estos son capaces de identificar comunidades solapadas, siendo aún menor el número de algoritmos que lo hacen en redes dirigidas y/o ponderadas. Así, este Trabajo Fin de Máster presenta un algoritmo que detecta comunidades solapadas en redes sociales dirigidas y/o ponderadas que, basándose en las ideas de amistad y liderazgo presentes en estas redes, no solo revela las comunidades identificadas, sino que también especifica quiénes son sus líderes. El algoritmo se describe en detalle y sus resultados se comparan con los obtenidos por otros algoritmos de detección de comunidades solapadas destacados en la literatura científica.ABSTRACT:With the recent increasing popularity of social networking services, such as Facebook or Twitter, community detection has become a problem of considerable interest. Although there are more than a hundred algorithms that find communities in social networks, only a few are able to detect overlapping communities, and an even smaller number of them do it in directed and/or weighted networks. For this reason, this Master’s Thesis presents an algorithm that detects overlapping communities in directed and/or weighted social networks, which—based on the ideas of friendship and leadership in these networks—not only revels the communities identified, but also specifies who their leaders are. The algorithm is described in detail and its results are compared with those obtained by prominent overlapping community detection algorithms found in the scientific literature.Máster en Matemáticas y Computació

Design and implementation of a tool for the comparison of community detection algorthms in graphs

RESUMEN: En la última década, la aparición de servicios como Facebook o Twitter ha dado como resultado un renovado interés en el análisis de redes sociales, siendo la detección de comunidades uno de los principales problemas que se han abordado. La detección de comunidades consiste en organizar los vértices de un grafo en grupos densamente conectados entre sí.Apesar de que se han propuesto decenas de algoritmos y varios generadores de grafos para comprobar su eficacia, la prueba de los mismos no ha recibido gran atención en la literatura: ésta suele limitarse a la aplicación del algoritmo propuesto a un conjunto de grafos cuya estructura es conocida de antemano o a la selección de los parámetros de un generador de grafos que permitan obtener redes estructuralmente sencillas. Esto supone un gran problema ya que no se puede afirmar qué método es mejor, por lo que, en la práctica, la elección del algoritmo a usar vendrá determinada por factores que nada tienen que ver con su eficiencia (por ejemplo, su popularidad o la reputación de su autor). Por ello, este Proyecto Fin de Carrera ha diseñado e implementado una aplicación que permite comparar algoritmos de detección de comunidades de manera imparcial. Ésta se ha diseñado de tal manera que los usuarios pueden añadirle algoritmos, generadores de grafos y medidas de evaluación de resultados, para lo cual se ha hecho uso de un lenguaje multiplataforma de propósito general, como Java. La aplicación obtiene los grafos a partir de los generadores suministrados y se los envía a los algoritmos para que éstos le devuelvan la estructura de comunidades detectada. Así, con las medidas de evaluación oportunas, puede determinar qué algoritmo se comporta mejor. Asimismo, se ha implementado implementar un mecanismo para que la ejecución del código de los componentes suministrados sea segura, de manera que un usuario malintencionado no pueda ejecutar código que sea capaz de afectar a la seguridad de la máquina. El diseño e implementación de esta aplicación se han llevado a cabo siguiendo la metodología MÉTRICA en desarrollo orientado a objetos.ABSTRACT: In the last decade, the appearance of services such as Facebook or Twitter has allowed for a renewed interest in social network analysis, being community detection one of the main problems tackled. Community detection consists in organizing the vertices of a graph in groups that permit them to be densely connected between each other. Despite the fact that many different algorithms and graph generators to test the efficiency of those algorithms have been proposed, that testing has not been duly treated in the literature: it usually is limited to the application of the proposed algorithm on a set of graphs whose structure is known in advance or the selection of the parameters of a graph generator that permit obtaining structurally simple networks. This becomes a great problem due to the fact that it cannot be ascertained what method is best, thus in practice choosing what algorithm to use will become conditioned by factors that have nothing to do with its efficiency (e.g., its popularity or the reputation of its author). For this reason, this Final Degree Project designed and implemented an application that permits comparing community detection algorithms in graphs in an impartial fashion. Itwas developed in such a manner that users can add algorithms, graph generators and measures for comparing results, for which a multipurpose and multiplatform programming language was used, in this case Java. The application obtains graphs from the generators included in it and sends them to the algorithms for these to return the community structure detected. Thus, with the appropriate measures, it is able to determine what algorithm does best. Also, a mechanism was implemented so as to ensure that the execution of these modules is safe, so that a malicious user cannot execute code that potentially puts the machine security at risk. The design and implementation of this application were done using the MÉTRICA methodology for object-oriented developments.Ingeniería en Informátic

Actividad en el ámbito de la Interacción Persona-Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI.

Se presenta la actividad en el ámbito de la Interacción Persona- Ordenador de un equipo de trabajo integrado en el grupo de investigación MCFLAI (Mathematics & Computation: Foundations, Learning, Artificial Intelligence) de la Universidad de Cantabria.Esta comunicación ha sido financiada por el proyecto “Development and validation of software tools and methodologies to provide individualized feedback and automatic performance assessment in programming learning” (convocatoria financiada por contrato programa Gobierno de Cantabria-UC) y a través de la beca de doctorado industrial DI27, concedida a Santos Bringas en la convocatoria del Programa de Doctorados Industriales 2020 (convocatoria financiada por la Universidad de Cantabria, el Gobierno de Cantabria y el Banco Santander)

Evolution of Medical Students' Perception of the Patient's Right to Privacy

During clinical rotations, medical students experience situations in which the patients' right to privacy may be violated. The aim of this study is to analyze medical students' perception of clinical situations that affect patients' right to privacy, and to look for the influential factors that may contribute to the infringement on their rights, such as the students' age, sex, academic year or parents' educational level. A cross-sectional study was conducted with a survey via "Google Drive". It consisted of 16 questions about personal information, 24 questions about their experience when rotating and 21 questions about their opinion concerning several situations related to the right to privacy. A total of 129 medical students from various Spanish medical schools participated. Only 31% of 3rd-6th year students declared having signed a confidentiality agreement when starting their clinical practice, and most students (52%) reported that doctors "sometimes", "rarely" or "never" introduce themselves and the students when entering the patients' rooms. Additionally, about 50% of all students reported that they would take a picture of a patient's hospitalization report without his/her (consent), which would be useful for an assignment. Important mistakes during medical students' rotations have been observed, as well as a general lack of knowledge regarding patient's right to privacy among Spanish medical students. Men and older students showed better knowledge of current legislation, as well as those whose parents were both university-educated and those in higher academic years.Clinical practiceHealth policy teachingMedical studentsPatient consentPrivac

Risk model for prostate cancer using environmental and genetic factors in the spanish multi-case-control (MCC) study

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model

Risk model for prostate cancer using environmental and genetic factors in the spanish multi-case-control (MCC) study

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model