This thesis brings a comprehensive view on the construction, usage and interpretation of composite indicators. Methods and techniques, which can be used for constructing composite indicators, are introduced. The focus is on their contribution to the transparent solution of the problem of correlation and compensability among underlying indicators. Transparency in construction of composite indicators is a crucial requirement for obtaining reliable results and their correct interpretation. The thesis consists of two main parts. The first part is theoretically oriented. First, the problem of adequacy and subsequently a measurement of the phenomenon by means of statistical indicators are discussed. Different methods for data normalization, setting a weighting scheme and aggregation are introduced and compared. These three steps are considered to be crucial in a process of constructing a composite indicator and thus, they are the core of the thesis. The aim is to investigate an interaction of normalization methods, weight-setting and aggregation methods, since these steps are not separate. The second part of the thesis consists of two comprehensive cases. Theoretical findings are applied and empirically verified in these cases. I investigated a robustness of the composite indicator depending on a combination of selected methods of normalization, setting weights and aggregation on a set of Europe 2020 indicators. Whereas this first case dealt with the comparative analysis of methods, the second case is focused purely on one issue -- university ranking. The proposed method reacts to criticism of currently published university rankings and takes into account specifics of the particular university as well as the exogenous background characteristics. The main added value rests in a contribution to a discussion about the improvement of construction and overall quality of composite indicators including their interpretation. I pointed out the main concerns and difficulties of composite indicators that often remain unnoticed by users and even constructors. The conclusion brings several beneficial findings, which can be used for the construction of a composite indicator and an interpretation of final scores and ranking. This work can also serve as a scientific ground for further research and development of the methodology of constructing composite indicators.

A análise da expressão gênica através de dados gerados em experimentos de microarrays de DNA vem possibilitando uma melhor compreensão da dinâmica e dos mecanismos envolvidos nos processos celulares ao nível molecular. O aprimoramento desta análise é crucial para o avanço do conhecimento sobre as bases moleculares das neoplasias e para a identificação de marcadores moleculares para uso em diagnóstico, desenho de novos medicamentos em terapias anti-tumorais. Este trabalho tem como objetivos o desenvolvimento de modelos de análise desses dados, propondo uma nova forma de normalização de dados provenientes de microarrays e dois modelos para a construção de redes regulatórias de expressão gênica, sendo uma baseada na conectividade dinâmica entre diversos genes ao longo do ciclo celular e a outra que resolve o problema da dimensionalidade, em que o número de experimentos de microarrays é menor que o número de genes. Apresenta-se, ainda, um pacote de ferramentas com uma interface gráfica de fácil uso contendo diversas técnicas de análise de dados já conhecidas como também as abordagens propostas neste trabalho. / The analyses of DNA microarrays gene expression data are allowing a better comprehension of the dynamics and mechanisms involved in cellular processes at the molecular level. In the cancer field, the improvement of gene expression interpretation is crucial to better understand the molecular basis of the neoplasias and to identify molecular markers to be used in diagnosis and in the design of new anti-tumoral drugs. The main goals of this work were to develop a new method to normalize DNA microarray data and two models to construct gene expression regulatory networks. One method analyses the dynamic connectivity between genes through the cell cycle and the other solves the dimensionality problem in regulatory networks, meaning that the number of experiments is lower than the number of genes. We also developed a toolbox with a user-friendly interface, displaying several established statistical methods implemented to analyze gene expression data as well as the new approaches presented in this work.

Η τεχνολογία των μικροσυστοιχιών επιτρέπει τη μέτρηση των επιπέδων έκφρασης χιλιάδων γονιδίων ταυτόχρονα σε ένα μόνο πείραμα δημιουργώντας έτσι ένα τεράστιο σύνολο δεδομένων για ανάλυση. Για να είναι δυνατή η εξαγωγή σημαντικής πληροφορίας για το υπό μελέτη βιολογικό σύστημα, έχουν χρησιμοποιηθεί διάφορες μέθοδοι προεπεξεργασίας και ανάλυσης των δεδομένων. Στις μεθόδους προεπεξεργασίας των δεδομένων συμπεριλαμβάνονται και οι μέθοδοι κανονικοποίησης. Σκοπός της κανονικοποίησης είναι η ελαχιστοποίηση των συστηματικών σφαλμάτων που εντοπίζονται στα εκτιμώμενα επίπεδα έκφρασης των γονιδίων, έτσι ώστε οι εμφανιζόμενες διαφορές τους να οφείλονται κυρίως σε βιολογικούς παράγοντες. Επίσης, η κανονικοποίηση καθιστά εφικτή τη σύγκριση των επιπέδων έκφρασης δεδομένων από περισσότερες της μίας μικροσυστοιχίες. Στην παρούσα διπλωματική εργασία παρατίθεται μια ανασκόπηση των μεθόδων κανονικοποίησης για δεδομένα γονιδιακής έκφρασης cDNA μικροσυστοιχιών καθώς και μια σύγκριση των αναλυόμενων μεθόδων κανονικοποίησης. / Microarray technology allows the measurement of gene expression levels of thousands of genes simultaneously in a single experiment, therefore creating a vast set of data for analysis. In order to be able to extract the most essential information for the biological system under examination in a specific microarray, various methods are used for data pre-processing and analysis. These data pre-processing methods also include normalization methods. The purpose of normalization is the minimization of the systematic errors that are found in the estimated gene expression levels, so as the observed biological differences be due mainly to biological factors. Furthermore, the normalization makes possible the comparison of gene expression levels of data from more than one microarrays. In the present thesis a review of the normalization methods for gene expression microarray data is presented, as well as a comparison between the analysed normalization methods.

A análise da expressão gênica através de dados gerados em experimentos de microarrays de DNA vem possibilitando uma melhor compreensão da dinâmica e dos mecanismos envolvidos nos processos celulares ao nível molecular. O aprimoramento desta análise é crucial para o avanço do conhecimento sobre as bases moleculares das neoplasias e para a identificação de marcadores moleculares para uso em diagnóstico, desenho de novos medicamentos em terapias anti-tumorais. Este trabalho tem como objetivos o desenvolvimento de modelos de análise desses dados, propondo uma nova forma de normalização de dados provenientes de microarrays e dois modelos para a construção de redes regulatórias de expressão gênica, sendo uma baseada na conectividade dinâmica entre diversos genes ao longo do ciclo celular e a outra que resolve o problema da dimensionalidade, em que o número de experimentos de microarrays é menor que o número de genes. Apresenta-se, ainda, um pacote de ferramentas com uma interface gráfica de fácil uso contendo diversas técnicas de análise de dados já conhecidas como também as abordagens propostas neste trabalho. / The analyses of DNA microarrays gene expression data are allowing a better comprehension of the dynamics and mechanisms involved in cellular processes at the molecular level. In the cancer field, the improvement of gene expression interpretation is crucial to better understand the molecular basis of the neoplasias and to identify molecular markers to be used in diagnosis and in the design of new anti-tumoral drugs. The main goals of this work were to develop a new method to normalize DNA microarray data and two models to construct gene expression regulatory networks. One method analyses the dynamic connectivity between genes through the cell cycle and the other solves the dimensionality problem in regulatory networks, meaning that the number of experiments is lower than the number of genes. We also developed a toolbox with a user-friendly interface, displaying several established statistical methods implemented to analyze gene expression data as well as the new approaches presented in this work.

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

Placement and compaction of the backfill behind an earth retaining wall may induce a vertical shear force at the soil-to-wall interface. This vertical shear force, or downdrag, is beneficial for the stability of the structure. A significant reduction in construction costs may result if the downdrag is accounted for during design. This potential reduction in costs is particularly interesting in the case of U.S. Army Corps of Engineers lock walls. A simplified procedure is available in the literature for estimating the downdrag force developed at the wall-backfill interface during backfilling of a retaining wall. However, finite element analyses of typical U.S. Army Corps of Engineers lock walls have shown that the magnitude of the downdrag force may decrease during operation of the lock with a rise in the water table in the backfill. They have also shown that pre- and post-construction stress paths followed by interface elements often involve simultaneous changes in shear and normal stresses and unloading-reloading. The hyperbolic formulation for interfaces (Clough and Duncan 1971) is accurate for modeling the interface response in the primary loading stage under constant normal stress. However, it has not been extended to model simultaneous changes in shear and normal stresses or unloading-reloading of the interface. The purpose of this research was to develop an interface model capable of giving accurate predictions of the interface response under field loading conditions, and to implement this model in a finite element program. In order to develop the necessary experimental data, a series of tests were performed on interfaces between concrete and two different types of sand. The tests included initial loading, staged shear, unloading-reloading, and shearing along complex stress paths. An extended hyperbolic model for interfaces was developed based on the results of the tests. The model is based on Clough and Duncan (1971) hyperbolic formulation, which has been extended to model the interface response to a variety of stress paths. Comparisons between model calculations and tests results showed that the model provides accurate estimates of the response of interfaces along complex stress paths. The extended hyperbolic model was implemented in the finite element program SOILSTRUCT-ALPHA, used by the U.S. Army Corps of Engineers for analyses of lock walls. A pilot-scale test was performed in the Instrumented Retaining Wall (IRW) at Virginia Tech that simulated construction and operation of a lock wall. SOILSTRUCT-ALPHA analyses of the IRW provided accurate estimates of the downdrag magnitude throughout inundation of the backfill. It is concluded that the extended hyperbolic model as implemented in SOILSTRUCT-ALPHA is adequate for routine analyses of lock walls. / Ph. D.