[pt] O desenvolvimento visto nos últimos anos em dispositivos móveis tem tornado dramático o aumento na quantidade de dados e informações disponíveis para publicitários ao redor do mundo. Custo computacional e tempo disponível para processar dados e ser capaz de distinguir verdadeiros usuários de anomalias ou ruído têm crescido. Assim, a criação de um método para detecção de outliers poderia apoiar melhor os pesquisadores de Marketing e aumentar sua precisão na compreensão do comportamento digital. Estudos atuais mostram que, até o momento, o uso de meta-algoritmos tem sido pouco usado para detecção de outliers. Meta-algoritmos tendem a trazer benefícios porque reduzem a dependência que um único algoritmo pode gerar. Esta dissertação propõe um design de meta-algoritmo que utiliza diferentes algoritmos para obter resultados de detecção de outliers melhores do que aqueles obtidos por apenas um único algoritmo: centrado em modelo e sequencial. A novidade da abordagem consiste em (i) explorar a técnica sequencial, utilizando algoritmos que são aplicados sequencialmente, no qual um algoritmo impacta o próximo e o resultado final é uma combinação dos resultados obtidos; (ii) centralizar a performance no modelo e não nos dados, o que significa que o ensemble é aplicado a todo o conjunto de dados ao mesmo tempo e; (iii) apoiar pesquisadores de marketing que precisem operar ciência de dados de forma mais robusta e coerente. / [en] Latest years evolution in mobile devices has increased dramatically the amount of data and available information for advertisers around the world. Computational cost and available time to process data and be able to distinguish true users from anomalies or noise has only increased. Thus, the creation of a method to detect outliers could support Marketing researchers and increase their precision in understanding online behavior. Recent studies showthat, so far, meta-algorithms have not been used to detect outliers. Metaalgorithms tend to bring benefits because they reduce dependency that a single algorithm can generate. This work proposes a sequential model-centric ensemble design that uses different algorithms in outlier detection to obtain better results than those obtained by a single algorithm. The novelty in this approach consists in: (i) exploring the sequential technique, using algorithms that impact the next one and whose results are a combination of previously obtained results; (ii) centralizing performance around the model and not the data, which means the ensemble is applied in the whole dataset and not on different subsamples; (iii) support Marketing researchers that need to operate data Science in a more robust and coherent way.

As depicted in Chapter I, 2-oxoglutarate- (2OG) dependent oxygenases are ubiquitous in living systems and display a wide range of cellular functions, spanning metabolism, transcription, and translation. Although functionally diverse, the 2OG oxygenases share a high degree of structural similarities between their catalytic sites. From a medicinal chemistry point of view, the combination of biological diversity and structural similarity presents a rather challenging task for the development of selective small molecules for functional studies in vivo. The non-selective metal chelator 8-hydroxyquinoline (8HQ) was used as a template for the generation of tool compound <b>I</b> for the KDM4 subfamily of histone demethylases via application of the Betti reaction. Structural analogue <b>II</b> was used as the corresponding negative control (Figure A). These compounds were characterised in vitro against a range of 2OG oxygenases and subsequently used for studies in cells. <b>I</b> displays selectivity for KDM4 and increases the level of the H3K9me3 histone mark in cells. It has an effect on the post-translational modification pattern of histone H3, but not other histones, and reduces the viability of lung cancer cells, but not normal lung cells, derived from the same patient. <b>I</b> also stabilises hypoxia-inducable factor HIF in cells via a mechanism which seems to be independent from prolyl hydroxylase inhibition. This work is described in Chapters II and III. The chemical biology research in epigenetics is complemented by qualitative analysis conducted in the social sciences at Said Business School. With a global view on how innovation occurs and may actively be fostered, Chapter IV focuses on the potential of epigenetics in drug discovery and how this process may actively be promoted within the framework of open innovation. Areas of focus include considerations of incremental and disruptive technology; how to claim, demarcate, and control the market; how knowledge brokering occurs; and insights about process, management, organisation, and culture of open innovation. In contrast to the open-skies approach adopted for the development of a tool compound in Chapters II and III, a focused-library approach was taken for the generation of a tool compound for the OGFOD1 ribosomal prolyl hydroxylase. The development of a suitable in vitro activity assay for OGFOD1 in Chapter V enabled the development of lead compound <b>III</b> in Chapter VI. <b>III</b> is selective for OGFOD1 against the structurally closely related prolyl hydroxylase PHD2.