Decision support continuum paradigm for cardiovascular disease: Towards personalized predictive models

Clinical decision making is a ubiquitous and frequent task physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e. knowledge and experience learnt from past lecture, research, literature, patients, etc.) for anticipating or ascertaining clinical problems based on clinical risk factors that they deemed to be most salient. However, with the inundation of health data and the confounding characteristics of diseases, more effective clinical prediction approaches are required to address these challenges. Approximately a few century ago, the first major transformation of medical practice took place as science-based approaches emerged with compelling results. Now, in the 21st century, new advances in science will once again transform healthcare. Data science has been postulated as an important component in this healthcare reform and has received escalating interests for its potential for ‘personalizing’ medicine. The key advantages of having personalized medicine include, but not limited to, (1) more effective methods for disease prevention, management and treatment, (2) improved accuracy for clinical diagnosis and prognosis, (3) provide patient-oriented personal health plan, and (4) cost containment. In view of the paramount importance of personalized predictive models, this thesis proposes 2 novel learning algorithms (i.e. an immune-inspired algorithm called the Evolutionary Data-Conscious Artificial Immune Recognition System, and a neural-inspired algorithm called the Artificial Neural Cell System for classification) and 3 continuum-based paradigms (i.e. biological, time and age continuum) for enhancing clinical prediction. Cardiovascular disease has been selected as the disease under investigation as it is an epidemic and major health concern in today’s world. We believe that our work has a meaningful and significant impact to the development of future healthcare system and we look forward to the wide adoption of advanced medical technologies by all care centres in the near future.Open Acces

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Selected Topics on Computed Tomography

This book is a research publication that covers developments within the Diagnostics field of study. The book is a collection of reviewed scholarly contributions written by different authors and edited by an expert with specific expertise. Each scholarly contribution represents a chapter which is complete in itself but related to the major topics and objectives. The target audience comprises scholars and specialists in the field

Load forecasting on the user‐side by means of computational intelligence algorithms

Nowadays, it would be very difficult to deny the need to prioritize sustainable development through energy efficiency at all consumption levels. In this context, an energy management system (EMS) is a suitable option for continuously improving energy efficiency, particularly on the user side. An EMS is a set of technological tools that manages energy consumption information and allows its analysis. EMS, in combination with information technologies, has given rise to intelligent EMS (iEMS), which, aside from lending support to monitoring and reporting functions as an EMS does, it has the ability to model, forecast, control and diagnose energy consumption in a predictive way. The main objective of an iEMS is to continuously improve energy efficiency (on-line) as automatically as possible. The core of an iEMS is its load modeling forecasting system (LMFS). It takes advantage of historical information on energy consumption and energy-related variables in order to model and forecast load profiles and, if available, generator profiles. These models and forecasts are the main information used for iEMS applications for control and diagnosis. That is why in this thesis we have focused on the study, analysis and development of LMFS on the user side. The fact that the LMFS is applied on the user side to support an iEMS means that specific characteristics are required that in other areas of load forecasting they are not. First of all, the user-side load profiles (LPs) have a higher random behavior than others, as for example, in power system distribution or generation. This makes the modeling and forecasting process more difficult. Second, on the user side --for example an industrial user-- there is a high number and variety of places that can be monitored, modeled and forecasted, as well as their precedence or nature. Thus, on the one hand, an LMFS requires a high degree of autonomy to automatically or autonomously generate the demanded models. And on the other hand, it needs a high level of adaptability in order to be able to model and forecast different types of loads and different types of energies. Therefore, the addressed LMFS are those that do not look only for accuracy, but also adaptability and autonomy. Seeking to achieve these objectives, in this thesis work we have proposed three novel LMFS schemes based on hybrid algorithms from computational intelligence, signal processing and statistical theory. The first of them looked to improve adaptability, keeping in mind the importance of accuracy and autonomy. It was called an evolutionary training algorithm (ETA) and is based on adaptivenetwork-based-fuzzy-inference system (ANFIS) that is trained by a multi-objective genetic algorithm instead of its traditional training algorithm. As a result of this hybrid, the generalization capacity was improved (avoiding overfitting) and an easily adaptable training algorithm for new adaptive networks based on traditional ANFIS was obtained. The second scheme deals with LMF autonomy in order to build models from multiple loads automatically. Similar to the previous proposal, an ANFIS and a MOGA were used. In this case, the MOGA was used to find a near-optimal configuration for the ANFIS instead of training it. The LMFS relies on this configuration to work properly, as well as to maintain accuracy and generalization capabilities. Real data from an industrial scenario were used to test the proposed scheme and the multi-site modeling and self-configuration results were satisfactory. Furthermore, other algorithms were satisfactorily designed and tested for processing raw data in outlier detection and gap padding. The last of the proposed approaches sought to improve accuracy while keeping autonomy and adaptability. It took advantage of dominant patterns (DPs) that have lower time resolution than the target LP, so they are easier to model and forecast. The Hilbert-Huang transform and Hilbert-spectral analysis were used for detecting and selecting the DPs. Those selected were used in a proposed scheme of partial models (PM) based on parallel ANFIS or artificial neural networks (ANN) to extract the information and give it to the main PM. Therefore, LMFS accuracy improved and the user-side LP noising problem was reduced. Additionally, in order to compensate for the added complexity, versions of self-configured sub-LMFS for each PM were used. This point was fundamental since, the better the configuration, the better the accuracy of the model; and subsequently the information provided to the main partial model was that much better. Finally, and to close this thesis, an outlook of trends regarding iEMS and an outline of several hybrid algorithms that are pending study and testing are presented.En el contexto energético actual y particularmente en el lado del usuario, el concepto de sistema de gestión energética (EMS) se presenta como una alternativa apropiada para mejorar continuamente la eficiencia energética. Los EMSs en combinación con las tecnologías informáticas dan origen al concepto de iEMS, que además de soportar las funciones de los EMS, tienen la capacidad de modelar, pronosticar, controlar y supervisar los consumos energéticos. Su principal objetivo es el de realizar una mejora continua, lo más autónoma posible y predictiva de la eficiencia energética. Este tipo de sistemas tienen como núcleo fundamental el sistema de modelado y pronóstico de consumos (Load Modeling and Forecasting System, LMFS). El LMFS está habilitado para pronosticar el comportamiento futuro de cargas y, si es necesario, de generadores. Es sobre estos pronósticos sobre los cuales el iEMS puede realizar sus tareas automáticas y predictivas de optimización y supervisión. Los LMFS en el lado del usuario son el foco de esta tesis. Un LMFS en el lado del usuario, diseñado para soportar un iEMS requiere o demanda ciertas características que en otros contextos no serían tan necesarias. En primera estancia, los perfiles de los usuarios tienen un alto grado de aleatoriedad que los hace más difíciles de pronosticar. Segundo, en el lado del usuario, por ejemplo en la industria, el gran número de puntos a modelar requiere que el LMFS tenga por un lado, un nivel elevado de autonomía para generar de la manera más desatendida posible los modelos. Por otro lado, necesita un nivel elevado de adaptabilidad para que, usando la misma estructura o metodología, pueda modelar diferentes tipos de cargas cuya procedencia pude variar significativamente. Por lo tanto, los sistemas de modelado abordados en esta tesis son aquellos que no solo buscan mejorar la precisión, sino también la adaptabilidad y autonomía. En busca de estos objetivos y soportados principalmente por algoritmos de inteligencia computacional, procesamiento de señales y estadística, hemos propuesto tres algoritmos novedosos para el desarrollo de un LMFS en el lado del usuario. El primero de ellos busca mejorar la adaptabilidad del LMFS manteniendo una buena precisión y capacidad de autonomía. Denominado ETA, consiste del uso de una estructura ANFIS que es entrenada por un algoritmo genético multi objetivo (MOGA). Como resultado de este híbrido, obtenemos un algoritmo con excelentes capacidades de generalización y fácil de adaptar para el entrenamiento y evaluación de nuevas estructuras adaptativas basadas en ANFIS. El segundo de los algoritmos desarrollados aborda la autonomía del LMFS para así poder generar modelos de múltiples cargas. Al igual que en la anterior propuesta usamos un ANFIS y un MOGA, pero esta vez el MOGA en vez de entrenar el ANFIS, se utiliza para encontrar la configuración cuasi-óptima del ANFIS. Encontrar la configuración apropiada de un ANFIS es muy importante para obtener un buen funcionamiento del LMFS en lo que a precisión y generalización respecta. El LMFS propuesto, además de configurar automáticamente el ANFIS, incluyó diversos algoritmos para procesar los datos puros que casi siempre estuvieron contaminados de datos espurios y gaps de información, operando satisfactoriamente en las condiciones de prueba en un escenario real. El tercero y último de los algoritmos buscó mejorar la precisión manteniendo la autonomía y adaptabilidad, aprovechando para ello la existencia de patrones dominantes de más baja resolución temporal que el consumo objetivo, y que son más fáciles de modelar y pronosticar. La metodología desarrollada se basa en la transformada de Hilbert-Huang para detectar y seleccionar tales patrones dominantes. Además, esta metodología define el uso de modelos parciales de los patrones dominantes seleccionados, para mejorar la precisión del LMFS y mitigar el problema de aleatoriedad que afecta a los consumos en el lado del usuario. Adicionalmente, se incorporó el algoritmo de auto configuración que se presentó en la propuesta anterior para hallar la configuración cuasi-óptima de los modelos parciales. Este punto fue crucial puesto que a mejor configuración de los modelos parciales mayor es la mejora en precisión del pronóstico final. Finalmente y para cerrar este trabajo de tesis, se realizó una prospección de las tendencias en cuanto al uso de iEMS y se esbozaron varias propuestas de algoritmos híbridos, cuyo estudio y comprobación se plantea en futuros estudios

Alternative strategies for deciphering the genetic architecture of childhood Pre-B acute lymphoblastic leukemia

La leucémie lymphoblastique aigüe (LLA) est une maladie génétique complexe. Malgré que cette maladie hématologique soit le cancer pédiatrique le plus fréquent, ses causes demeurent inconnues. Des études antérieures ont démontrées que le risque à la LLA chez l’enfant pourrait être influencé par des gènes agissant dans le métabolisme des xénobiotiques, dans le maintient de l’intégrité génomique et dans la réponse au stress oxydatif, ainsi que par des facteurs environnementaux. Au cours de mes études doctorales, j’ai tenté de disséquer davantage les bases génétiques de la LLA de l’enfant en postulant que la susceptibilité à cette maladie serait modulée, au moins en partie, par des variants génétiques agissant dans deux voies biologiques fondamentales : le point de contrôle G1/S du cycle cellulaire et la réparation des cassures double-brin de l’ADN. En utilisant une approche unique reposant sur l’analyse d’une cohorte cas-contrôles jumelée à une cohorte de trios enfants-parents, j’ai effectué une étude d’association de type gènes/voies biologiques candidats. Ainsi, j’ai évaluer le rôle de variants provenant de la séquence promotrice de 12 gènes du cycle cellulaire et de 7 gènes de la voie de réparation de l’ADN, dans la susceptibilité à la LLA. De tels polymorphismes dans la région promotrice (pSNPs) pourraient perturber la liaison de facteurs de transcription et mener à des différences dans les niveaux d’expression des gènes pouvant influencer le risque à la maladie. En combinant différentes méthodes analytiques, j’ai évalué le rôle de différents mécanismes génétiques dans le développement de la LLA chez l’enfant. J’ai tout d’abord étudié les associations avec gènes/variants indépendants, et des essaies fonctionnels ont été effectués afin d’évaluer l’impact des pSNPs sur la liaison de facteurs de transcription et l’activité promotrice allèle-spécifique. Ces analyses ont mené à quatre publications. Il est peu probable que ces gènes de susceptibilité agissent seuls; j’ai donc utilisé une approche intégrative afin d’explorer la possibilité que plusieurs variants d’une même voie biologique ou de voies connexes puissent moduler le risque de la maladie; ces travaux ont été soumis pour publication. En outre, le développement précoce de la LLA, voir même in utero, suggère que les parents, et plus particulièrement la mère, pourraient jouer un rôle important dans le développement de cette maladie chez l’enfant. Dans une étude par simulations, j’ai évalué la performance des méthodes d’analyse existantes de détecter des effets fœto-maternels sous un design hybride trios/cas-contrôles. J’ai également investigué l’impact des effets génétiques agissant via la mère sur la susceptibilité à la LLA. Cette étude, récemment publiée, fût la première à démontrer que le risque de la leucémie chez l’enfant peut être modulé par le génotype de sa mère. En conclusions, mes études doctorales ont permis d’identifier des nouveaux gènes de susceptibilité pour la LLA pédiatrique et de mettre en évidence le rôle du cycle cellulaire et de la voie de la réparation de l’ADN dans la leucémogenèse. À terme, ces travaux permettront de mieux comprendre les bases génétiques de la LLA, et conduiront au développement d’outils cliniques qui amélioreront la détection, le diagnostique et le traitement de la leucémie chez l’enfant.Childhood acute lymphoblastic leukemia (ALL) is a complex and heterogeneous genetic disease. Although it is the most common pediatric cancer, its etiology remains poorly understood. Previous studies provided evidence that childhood ALL might originate through the collective contribution of different genes controlling the efficiency of carcinogen metabolism, the capacity of maintaining DNA integrity and the response to oxidative stress, as well as environmental factors. In my doctoral research project I attempted to further dissect the genetic intricacies underlying childhood ALL. I postulated that a child’s susceptibility to ALL may be influenced, in part, by functional sequence variation in genes encoding components of two core biologic pathways: G1/S cell cycle control and DNA double-strand break repair. Using a unique two-tiered study design consisting of both unrelated ALL cases and healthy controls, as well as case-parent trios, I performed a pathway-based candidate-gene association study to investigate the role of sequence variants in the promoter regions of 12 candidate cell cycle genes and 7 DNA repair genes, in modulating ALL risk among children. Polymorphisms in promoter regions (pSNPs) could perturb transcription factor binding and lead to differences in gene expression levels that in turn could modify the risk of disease. To better depict the complex genetic architecture of childhood ALL, I used multiple analytical approaches. First, individual genes/variants were tested for association with disease, while functional in vitro validation was performed to evaluate the impact of the pSNPs on differential transcription factor binding and allele-specific promoter activity. These analyses led to four published articles. Given that these genes are not likely to act alone to confer disease risk I used an integrative approach to explore the possibility that combinations of functionally relevant pSNPs among several components of the same or of interconnected pathways, could contribute to modified childhood ALL risk either through pathway-specific or epistatic effects; this work was recently submitted for publication. Finally, childhood ALL is thought to arise in utero suggesting that the parents, and in particular the mother, may play an important role in shaping disease susceptibility in their offspring. Using simulations, I investigated the performance of existing methods to test for maternal genotype associations using a case-parent trio/case-control hybrid design, and then assessed the impact of maternally-mediated genetic effects on ALL susceptibility among children. This published work was the first to show that the mother’s genotype can indeed influence the risk of leukemia in children, further corroborating the importance of considering parentally-mediated effects in the study of early-onset diseases. In conclusion, my doctoral work lead to the identification of novel genetic susceptibility loci for childhood ALL and provided evidence for the implication of the cell cycle control and DNA repair pathways in leukemogenesis. Better elucidation of the genetic mechanisms underlying the pathogenesis of ALL in children could be of great diagnostic value and provide data to help guide risk-directed therapy and improve disease management and outcome. Ultimately, this study brings us one step closer to unraveling the genetic architecture of childhood ALL and provides a stepping-stone towards disease prevention