Abstract
Classification plays an important role in medicine, especially for medical diagnosis. Real-world medical applications often require classifiers that minimize the total cost, including costs for wrong diagnosis (misclassifications costs) and diagnostic test costs (attribute costs). There are indeed many reasons for considering costs in medicine, as diagnostic tests are not free and health budgets are limited. In this chapter, the authors have defined strategies for cost-sensitive learning. They have developed an algorithm for decision tree induction that considers various types of costs, including test costs, delayed costs and costs associated with risk. Then they have applied their strategy to train and to evaluate cost-sensitive decision trees in medical data. Generated trees can be tested following some strategies, including group costs, common costs, and individual costs. Using the factor of "risk" it is possible to penalize invasive or delayed tests and obtain patient-friendly decision trees. © 2010, IGI Global.

Abstract
The University of Porto has a well-established set of specialized libraries serving the research and student population of its 14 schools. Thematic digital libraries can be valuable for organizing specific collections and for supporting emergent communities. This work focuses on two case studies, one in the area of the Fine Arts and the other in the area of Food and Nutrition. For building both digital libraries we propose to use the existing university repository infrastructure and to establish a metadata workflow that makes use of available descriptions in the library catalogues and in the university information system. We expect that such an approach, which takes into account the institutional context and resources, can be used in other collections at our university and inspire similar initiatives elsewhere.

Abstract
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). In fact, as in other professional areas, both diagnostic tests and its associated misclassification errors can have significant financial or human costs, including the use of unnecessary resource and patient safety issues. This chapter presents some concepts related to cost-sensitive learning and cost-sensitive classification and its application to medicine. Different types of costs are also present, with an emphasis on diagnostic tests and misclassification costs. In addition, an overview of research in the area of cost-sensitive learning is given, including current methodological approaches. Finally, current methods for the cost-sensitive evaluation of classifiers are discussed. © 2009, IGI Global.

Abstract

Abstract
Engineering frameworks are currently required to support the easy, low-cost, modular and integrated development of production systems, addressing the emergent requirements of re-configurability, responsiveness and robustness. This paper discusses the integration of High-level Petri net-based service-oriented frameworks with 2D/3D engineering tools, allowing the digitally design, configuration, validation, simulation, control and monitoring of production systems, in an integrated manner. An experimental case study was implemented, based on the Petri nets development toolKit (PndK) development framework, to validate the proposed concepts. © 2009 IFAC.

Abstract
The protein folding problem, i.e. the identification of the rules that determine the acquisition of the native, functional, three-dimensional structure of a protein from its linear sequence of amino-acids, still is a major challenge in structural molecular biology. Moreover, the identification of a series of neurodegenerative diseases as protein unfolding/misfolding disorders highlights the importance of a detailed characterisation of the molecular events driving the unfolding and misfolding processes in proteins. One way of exploring these processes is through the use of molecular dynamics simulations. The analysis and comparison of the enormous amount of data generated by multiple protein folding or unfolding simulations is not a trivial task, presenting many interesting challenges to the data mining community. Considering the central role of the hydrophobic effect in protein folding, we show here the application of two data mining methods - hierarchical clustering and association rules - for the analysis and comparison of the solvent accessible surface area (SASA) variation profiles of each one of the 127 amino-acid residues in the amyloidogenic protein Transthyretin, across multiple molecular dynamics protein unfolding simulations. © 2010, IGI Global.