n this chapter, we study the application of existing entity resolution (ER) techniques on a real-world multi-source genealogical dataset. Our goal is to identify all persons involved in various notary acts and link them to their birth, marriage, and death certificates. We analyze the influence of additional ER features, such as name popularity, geographical distance, and co-reference information on the overall ER performance. We study two prediction models: regression trees and logistic regression. In order to evaluate the performance of the applied algorithms and to obtain a training set for learning the models we developed an interactive interface for getting feedback from human experts. We perform an empirical evaluation on the manually annotated dataset in terms of precision, recall, and F-score. We show that using name popularity, geographical distance together with co-reference information helps to significantly improve ER results

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We describe three different approaches to the Context Aware Movie Recommendation (CAMRa) challenge. Each approach is based on different machine learning techniques: two are nearest neighbor approaches, one is based on inductive logic programming. The results obtained with the three techniques are compared.

Molecular biology and all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines and tools sharing the '-omics' suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type. The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First, the amount of available experimental data is not a limiting factor any more; on the contrary, there is a plethora of it. Given the research question, the challenge has shifted towards identifying the relevant pieces of information and making sense out of it (a 'data mining' issue). Second, rather than focus on components in isolation, we can now try to understand how biological systems behave as a result of the integration and interaction between the individual components that one can now monitor simultaneously, so called 'systems biology'. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms. This book contains the scientific contributions presented at the Fourth International Workshop on Machine Learning in Systems Biology (MLSB'2010), held in Edinburgh from October 15 to 16, 2010. The workshop was organized as a core event of the PASCAL2 Network of Excellence, under the IST programme of European Union. The aim of the workshop was to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. The technical program of the workshop consisted of invited lectures, oral presentations and poster presentations. Invited lectures were given by Nir Friedman,Ursula Kummer, Hans Lehrach, Florence d'Alché-Buc, and Vebjorn Ljosa. Sixteen oral presentations were given, for which extended abstracts are included in this book: these were selected from 24 submissions, each reviewed by three members of the scientific program committee. Twenty-five poster presentations were given, for which abstracts of varying length are included here. We would like to thank all the people contributing to the technical programme, the scientific program committee, the local organizers and the sponsors for making the workshop possible.

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Entity Resolution (ER) is the process of identifying references referring to the same entity from one or more data sources. In the ER process, most existing approaches exploit the content information of references, categorized as contentbased ER, or additionally consider linkage information among references, categorized as context-based ER. However, in new applications of ER, such as in the genealogical domain, the very limited linkage information among references results in a disjoint graph in which the existing content-/context-based ER techniques have very limited applicability. Therefore, in this paper we propose first, to use the homophily principle for augmentation of the original input graph by connecting the potential similar references, and second, to use a Random Walk based approach to consider contextual information available for each reference in the augmented graph. We evaluate the proposed method by applying it to a large genealogical dataset and we succeed to predict 420,000 reference matches with precision 92% and discover six novel and informative patterns among them which can not be detected in the original disjoint graph.

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