Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

Discovery and validation of serologic biomarkers for the diagnosis and prognosis of invasive candidiasis by computational immunomics

Invasive candidiasis (IC) is an opportunistic systemic mycosis caused by Candida species (commonly Candida albicans) that continues to pose a significant public health problem worldwide. Despite great advances in antifungal therapy and changes in clinical practices, IC remains a major infectious cause of morbidity and mortality in severely immunocompromised or critically ill patients, and further accounts for substantial healthcare costs. Its impact on patient clinical outcome and economic burden could be ameliorated by timely initiation of appropriate antifungal therapy. However, early detection of IC is extremely difficult because of its unspecific clinical signs and symptoms, and the inadequate accuracy and time delay of the currently available diagnostic or risk stratification methods. In consequence, the diagnosis of IC is often attained in advanced stages of infection (leading to delayed therapeutic interventions and ensuing poor clinical outcomes) or, unfortunately, at autopsy. In addition to the difficulties encountered in diagnosing IC at an early stage, the initial therapeutic decision-making process is also hindered by the insufficient accuracy of the currently available tools for predicting clinical outcomes in individual IC patients at presentation. Therefore, it is not surprising that clinicians are generally unable to early detect IC, and identify those IC patients who are most likely to suffer fatal clinical outcomes and may benefit from more personalized therapeutic strategies at presentation. Better diagnostic and prognostic biomarkers for IC are thus needed to improve the clinical management of this life-threatening and costly opportunistic fungal infection...

Development of a novel platform for high-throughput gene design and artificial gene synthesis to produce large libraries of recombinant venom peptides for drug discovery

Tese de Doutoramento em Ciências Veterinárias na Especialidade de Ciências Biológicas e Biomédicas

Tools For Spatiotemporally Specific Proteomic Analysis In Multicellular Organisms

The emergence of mass spectrometry-based proteomics has revolutionized the study of proteins and their abundances, functions, interactions, and modifications. However, in a multicellular organism, it is difficult to monitor dynamic changes in protein synthesis in a specific cell type within its native environment. In this thesis, we describe methods that enable the metabolic labeling, purification, and analysis of proteins in specific cell types and during defined periods in live animals. We first engineered a eukaryotic phenylalanyl-tRNA synthetase (PheRS) to selectively recognize the unnatural L-phenylalanine analog p-azido-L-phenylalanine (Azf). Using Caenorhabditis elegans, we expressed the engineered PheRS in a cell type of choice (i.e. body wall muscles, intestinal epithelial cells, neurons, pharyngeal muscles), permitting proteins in those cells -- and only those cells -- to be labeled with azides. Labeled proteins are therefore subject to "click" conjugation to cyclooctyne-functionalized affnity probes, separation from the rest of the protein pool and identification by mass spectrometry. By coupling our methodology with heavy isotopic labeling, we successfully identified proteins -- including proteins with previously unknown expression patterns -- expressed in targeted subsets of cells. While cell types like body wall or pharyngeal muscles can be targeted with a single promoter, many cells cannot; spatiotemporal selectivity typically results from the combinatorial action of multiple regulators. To enhance spatiotemporal selectivity, we next developed a two-component system to drive overlapping -- but not identical -- patterns of expression of engineered PheRS, restricting labeling to cells that express both elements. Specifically, we developed a split-intein-based split-PheRS system for highly efficient PheRS-reconstitution through protein splicing. Together, these tools represent a powerful approach for unbiased discovery of proteins uniquely expressed in a subset of cells at specific developmental stages.

Towards a 21st-century roadmap for biomedical research and drug discovery: consensus report and recommendations

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies, and the corporate and nongovernmental organisation (NGO) sectors, in this consensus report, we analyse, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathway-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this.