Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Robust approaches are needed for integration and validation of independently-collected datasets which may contribute to a common hypothesis. Previously, attempts at meta-analysis have contributed to solutions to this problem. As an alternative, for microarray data from multiple highly similar biological experimental designs, a more direct combined approach is possible. In this thesis, a novel approach is described for microarray combined analysis, including gene-level unification into a virtual platform followed by normalization and a method for ranking candidate genes based on co-expression information - called Coex-Rank. We applied this approach to our Sppar (a PPARγ mutant) dataset, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank method from a biological perspective. We also performed analysis to other PPARγ-related microarray datasets. From the perspective of gene sets, we observed that up-regulated genes from mice treated with the PPARγ ligand rosiglitazone were significantly down-regulated in mice with a global knock-in dominant-negative mutation of PPARγ. Integrated with publicly available PPRE (PPAR Response Element) datasets, we found that the genes which were most up-regulated by rosiglitazone treatment and which were also down-regulated by the global knock-in mutation of PPARγ were robustly enriched in PPREs near transcription start sites. In addition, we identified several potential PPARγ targets in the aorta and mesenteric artery for further experimental validation, such as Rhobtb1 and Rgs5.

How to properly aggregate indirect information is more and more important. In this dissertation, we will present two aspects of the issue: indirect comparison of treatment effects and aggregation of ordered-based rank data. / Statistics

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KRAS is a gene that plays a very important role in the initiation and development of several types of cancer. In particular, 90% of human pancreatic cancers are due to KRAS mutations. KRAS is difficult to target directly and a promising therapeutic path is its indirect inactivation by targeting one of its Synthetic Lethal Partners (SLPs). A gene G is a Synthetic Lethal Partner of KRAS if the simultaneous perturbation of KRAS and G leads to cell death. In the past, efforts to identify KRAS SLPs with high-throughput RNAi screens have been performed. These studies have reported only few top-ranked SLPs. To our knowledge, these screens have never been considered in combination for further examination. This thesis employs integrative analysis of the published screens, utilizing additional, independent data aiming at the detection of more robust therapeutic targets. To this aim, RankSLP, a novel statistical analysis approach was implemented, which for the first time i) consistently integrates existing KRAS-specific RNAi screens, ii) consistently integrates and normalizes the results of various ranking methods, iii) evaluates its findings with the use of external data and iv) explores the effects of random data inclusion. This analysis was able to predict novel SLPs of KRAS and confirm some of the existing ones.

KRAS is a gene that plays a very important role in the initiation and development of several types of cancer. In particular, 90% of human pancreatic cancers are due to KRAS mutations. KRAS is difficult to target directly and a promising therapeutic path is its indirect inactivation by targeting one of its Synthetic Lethal Partners (SLPs). A gene G is a Synthetic Lethal Partner of KRAS if the simultaneous perturbation of KRAS and G leads to cell death. In the past, efforts to identify KRAS SLPs with high-throughput RNAi screens have been performed. These studies have reported only few top-ranked SLPs. To our knowledge, these screens have never been considered in combination for further examination. This thesis employs integrative analysis of the published screens, utilizing additional, independent data aiming at the detection of more robust therapeutic targets. To this aim, RankSLP, a novel statistical analysis approach was implemented, which for the first time i) consistently integrates existing KRAS-specific RNAi screens, ii) consistently integrates and normalizes the results of various ranking methods, iii) evaluates its findings with the use of external data and iv) explores the effects of random data inclusion. This analysis was able to predict novel SLPs of KRAS and confirm some of the existing ones.

O crescente volume de informações disponíveis na rede mundial de computadores, gera a necessidade do uso de ferramentas que sejam capazes de localizá-las e ordenálas, de forma cada vez mais precisa e que demandem cada vez menos recursos computacionais. Esta necessidade tem motivado pesquisadores a estudar e desenvolver modelos e técnicas que atendam esta demanda. Estudos recentes têm sinalizado que utilizar vários ordenamentos (rankings) previamente montados possibilita o retorno e ordenação de objetos de qualquer natureza com mais eficiência, principalmente pelo fato de haver uma redução no custo da busca pela informação. Este processo, conhecido como fusão de rankings, permite que se obtenha um ordenamento com base na opinião de diversos juízes (critérios), o que possibilita considerar um grande número de fontes, tanto geradas automaticamente como por especialistas. Entretanto os modelos propostos até então tem apresentado várias limitações na sua aplicação: desde a quantidade de rankings envolvidos até, principalmente, a utilização de rankings parciais. A proposta desta dissertação é apresentar um modelo de fusão de rankings que busca estabelecer um consenso entre as opiniões (rankings) dos diferentes juízes envolvidos, considerando distintos graus de relevância ou importância entre eles. A base desta proposta está na Análise de Preferência, um conjunto de técnicas que permite o tratamento da multidimensionalidade dos dados envolvidos. Ao ser testado em uma aplicação real, o modelo mostrou conseguir suprir algumas limitações apresentadas em outras abordagens, bem como apresentou resultados similares aos das aplicações originais. Esta pesquisa, ainda contribui, com a especificação de um sistema Web baseado em tecnologias open source, o qual permite que qualquer pessoa possa realizar a fusão de rankings. / The growing volume of available information on the web creates the need to use tools that are capable of retrieve and ordering this information, ever more precise and using less computer resources. This need has motivated researchers to study and develop models and techniques that solve this problem. Recent studies have indicated that use multiple rankings previously mounted makes possible the return and sorting of the objects of any kind with more efficiency, mainly because there is a reduction in the cost of searching for information. This process, called ranking fusion, provide a ranking based on the opinion of several judges (criteria), considering a large number of sources, both generated automatically and also by specialists. However the proposed models have shown severe limitations in its application: from the amount involved rankings to the use of partial rankings. The proposal of this dissertation is to show a model of ranking fusion that seeks to establish a consensus between the judgement (rankings) of the various judges involved, considering different degrees of relevance or importance among them. The baseline of this proposal is the Preference Analysis, a set of techniques that allows the treatment of multidimensional data handling. During tests in a real application, the model supplied some limitations presented by other approaches, and presented results similar to the original applications. Additionally, this research contributes with the specification of a web system based on open-sources technologies, enabling the realization of fusion rankings by anyone.

O crescente volume de informações disponíveis na rede mundial de computadores, gera a necessidade do uso de ferramentas que sejam capazes de localizá-las e ordenálas, de forma cada vez mais precisa e que demandem cada vez menos recursos computacionais. Esta necessidade tem motivado pesquisadores a estudar e desenvolver modelos e técnicas que atendam esta demanda. Estudos recentes têm sinalizado que utilizar vários ordenamentos (rankings) previamente montados possibilita o retorno e ordenação de objetos de qualquer natureza com mais eficiência, principalmente pelo fato de haver uma redução no custo da busca pela informação. Este processo, conhecido como fusão de rankings, permite que se obtenha um ordenamento com base na opinião de diversos juízes (critérios), o que possibilita considerar um grande número de fontes, tanto geradas automaticamente como por especialistas. Entretanto os modelos propostos até então tem apresentado várias limitações na sua aplicação: desde a quantidade de rankings envolvidos até, principalmente, a utilização de rankings parciais. A proposta desta dissertação é apresentar um modelo de fusão de rankings que busca estabelecer um consenso entre as opiniões (rankings) dos diferentes juízes envolvidos, considerando distintos graus de relevância ou importância entre eles. A base desta proposta está na Análise de Preferência, um conjunto de técnicas que permite o tratamento da multidimensionalidade dos dados envolvidos. Ao ser testado em uma aplicação real, o modelo mostrou conseguir suprir algumas limitações apresentadas em outras abordagens, bem como apresentou resultados similares aos das aplicações originais. Esta pesquisa, ainda contribui, com a especificação de um sistema Web baseado em tecnologias open source, o qual permite que qualquer pessoa possa realizar a fusão de rankings. / The growing volume of available information on the web creates the need to use tools that are capable of retrieve and ordering this information, ever more precise and using less computer resources. This need has motivated researchers to study and develop models and techniques that solve this problem. Recent studies have indicated that use multiple rankings previously mounted makes possible the return and sorting of the objects of any kind with more efficiency, mainly because there is a reduction in the cost of searching for information. This process, called ranking fusion, provide a ranking based on the opinion of several judges (criteria), considering a large number of sources, both generated automatically and also by specialists. However the proposed models have shown severe limitations in its application: from the amount involved rankings to the use of partial rankings. The proposal of this dissertation is to show a model of ranking fusion that seeks to establish a consensus between the judgement (rankings) of the various judges involved, considering different degrees of relevance or importance among them. The baseline of this proposal is the Preference Analysis, a set of techniques that allows the treatment of multidimensional data handling. During tests in a real application, the model supplied some limitations presented by other approaches, and presented results similar to the original applications. Additionally, this research contributes with the specification of a web system based on open-sources technologies, enabling the realization of fusion rankings by anyone.

O crescente volume de informações disponíveis na rede mundial de computadores, gera a necessidade do uso de ferramentas que sejam capazes de localizá-las e ordenálas, de forma cada vez mais precisa e que demandem cada vez menos recursos computacionais. Esta necessidade tem motivado pesquisadores a estudar e desenvolver modelos e técnicas que atendam esta demanda. Estudos recentes têm sinalizado que utilizar vários ordenamentos (rankings) previamente montados possibilita o retorno e ordenação de objetos de qualquer natureza com mais eficiência, principalmente pelo fato de haver uma redução no custo da busca pela informação. Este processo, conhecido como fusão de rankings, permite que se obtenha um ordenamento com base na opinião de diversos juízes (critérios), o que possibilita considerar um grande número de fontes, tanto geradas automaticamente como por especialistas. Entretanto os modelos propostos até então tem apresentado várias limitações na sua aplicação: desde a quantidade de rankings envolvidos até, principalmente, a utilização de rankings parciais. A proposta desta dissertação é apresentar um modelo de fusão de rankings que busca estabelecer um consenso entre as opiniões (rankings) dos diferentes juízes envolvidos, considerando distintos graus de relevância ou importância entre eles. A base desta proposta está na Análise de Preferência, um conjunto de técnicas que permite o tratamento da multidimensionalidade dos dados envolvidos. Ao ser testado em uma aplicação real, o modelo mostrou conseguir suprir algumas limitações apresentadas em outras abordagens, bem como apresentou resultados similares aos das aplicações originais. Esta pesquisa, ainda contribui, com a especificação de um sistema Web baseado em tecnologias open source, o qual permite que qualquer pessoa possa realizar a fusão de rankings. / The growing volume of available information on the web creates the need to use tools that are capable of retrieve and ordering this information, ever more precise and using less computer resources. This need has motivated researchers to study and develop models and techniques that solve this problem. Recent studies have indicated that use multiple rankings previously mounted makes possible the return and sorting of the objects of any kind with more efficiency, mainly because there is a reduction in the cost of searching for information. This process, called ranking fusion, provide a ranking based on the opinion of several judges (criteria), considering a large number of sources, both generated automatically and also by specialists. However the proposed models have shown severe limitations in its application: from the amount involved rankings to the use of partial rankings. The proposal of this dissertation is to show a model of ranking fusion that seeks to establish a consensus between the judgement (rankings) of the various judges involved, considering different degrees of relevance or importance among them. The baseline of this proposal is the Preference Analysis, a set of techniques that allows the treatment of multidimensional data handling. During tests in a real application, the model supplied some limitations presented by other approaches, and presented results similar to the original applications. Additionally, this research contributes with the specification of a web system based on open-sources technologies, enabling the realization of fusion rankings by anyone.

Los sistemas online para compartir fotos proporcionan una valiosa fuente de contenidos generado por el usuario (UGC). La mayor a de los sistemas de re- cuperaci on de im agenes Web utilizan las anotaciones textuales para rankear los resultados, sin embargo estas anotaciones no s olo ilustran el contenido visual de una imagen, sino que tambi en describen situaciones subjetivas, espaciales, temporales y sociales, que complican la tarea de b usqueda basada en palabras clave. La investigaci on en esta tesis se centra en c omo mejorar la recuperaci on de im agenes en sistemas de gran escala, es decir, la Web, combinando informaci on proporcionada por los usuarios m as el contenido visual de las im agenes. En el presente trabajo se exploran distintos tipos de UGC, tales como anotaciones de texto, anotaciones visuales, y datos de click-through, as como diversas t ecnicas para combinar esta informaci on con el objetivo de mejorar la recuperaci on de im agenes usando informaci on visual. En conclusi on, la investigaci on realizada en esta tesis se centra en la impor- tancia de incluir la informaci on visual en distintas etapas de la recuperaci on de contenido. Combinando informaci on visual con otras formas de UGC, es posible mejorar signi cativamente el rendimiento de un sistema de recuperaci on de im agenes y cambiar la experiencia del usuario en la b usqueda de contenidos multimedia en la Web. / Online photo sharing systems provide a valuable source of user generated content (UGC). Most Web image retrieval systems use textual annotations to rank the results, although these annotations do not only illustrate the visual content of an image, but also describe subjective, spatial, temporal, and social dimensions, complicating the task of keyword based search. The research in this thesis is focused on how to improve the retrieval of images in large scale context , i.e. the Web, using information provided by users combined with visual content from images. Di erent forms of UGC are explored, such as textual annotations, visual annotations, and click-through-data, as well as di erent techniques to combine these data to improve the retrieval of images using visual information. In conclusion, the research conducted in this thesis focuses on the impor- tance to include visual information into various steps of the retrieval of media content. Using visual information, in combination with various forms of UGC, can signi cantly improve the retrieval performance and alter the user experience when searching for multimedia content on the Web. 1

In this thesis, we aim at improving the search result quality by utilizing the search intelligence (history of searches) available in the form of click-through data. We address two key issues, namely 1) relevance feedback extraction and fusion, and 2) deciphering search query intentions. Relevance Feedback Extraction and Fusion: The existing search engines depend heavily on the web linkage structure in the form of hyperlinks to determine the relevance and importance of the documents. But these are collective judgments given by the page authors and hence, prone to collaborated spamming. To overcome the spamming attempts and language semantic issues, it is also important to incorporate the user feedback on the documents' relevance. Since users can be hardly motivated to give explicit/direct feedback on search quality, it becomes necessary to consider implicit feedback that can be collected from search engine logs. Though a number of implicit feedback measures have been proposed in the literature, we have not been able to identify studies that aggregate those feedbacks in a meaningful way to get a final ranking of documents. In this thesis, we first evaluate two implicit feedback measures namely 1) click sequence and 2) time spent on the document for their content uniqueness. We develop a mathematical programming model to collate the feedbacks collected from different sessions into a single ranking of documents. We use Kendall's τ rank correlation to determine the uniqueness of the information content present in the individual feedbacks. The experimental evaluation on top 30 select queries from an actual search log data confirms that these two measures are not in perfect agreement and hence, incremental information can potentially be derived from them. Next, we study the feedback fusion problem in which the user feedbacks from various sessions need to be combined meaningfully. Preference aggregation is a classical problem in economics and we study a variation of it where the rankers, i.e., the feedbacks, possess different expertise. We extend the generalized Mallows' model to model the feedback rankings given in user sessions. We propose a single stage and two stage aggregation framework to combine different feedbacks into one final ranking by taking their respective expertise into consideration. We show that the complexity of the parameter estimation problem is exponential in number of documents and queries. We develop two scalable heuristics namely, 1) a greedy algorithm, and 2) a weight based heuristic, that can closely approximate the solution. We also establish the goodness of fit of the model by testing it on actual log data through log-likelihood ratio test. As the independent evaluation of documents is not available, we conduct experiments on synthetic datasets devised appropriately to examine the various merits of the heuristics. The experimental results confirm the possibility of expertise oriented aggregation of feedbacks by producing orderings better than both the best ranker as well as equi-weight aggregator. Motivated with this result, we extend the aggregation framework to hold infinite rankings for the meta-search applications. The aggregation results on synthetic datasets are found to be ensuring the extension fruitful and scalable. Deciphering Search Query Intentions: The search engine often retrieves a huge list of documents based on their relevance scores for a given query. Such a presentation strategy may work if the submitted query is very specific, homogeneous and unambiguous. But many a times it so happen that the queries posed to the search engine are too short to be specific and hence ambiguous to identify clearly the exact information need, (eg. "jaguar"). These ambiguous and heterogeneous queries invite results from diverse topics. In such cases, the users may have to sift through the entire list to find their needed information and that could be a difficult task. Such a task can be simplified by organizing the search results under meaningful subtopics, which would help the users to directly move on to their topic of interest and ignore the rest. We develop a method to determine the various possible intentions of a given short generic and ambiguous query using information from the click-through data. We propose a two stage clustering framework to co-cluster the queries and documents into intentions that can readily be presented whenever it is demanded. For this problem, we adapt the spectral bipartite partitioning by extending it to automatically determine the number of clusters hidden in the log data. The algorithm has been tested on selected ambiguous queries and the results demonstrate the ability of the algorithm in distinguishing among the user intentions.