Impact Evaluation of Interoperability Decision Variables on P2P Collaboration Performances

This article deals with the impact evaluation of interoperability decision variables on performance indicators of business processes. The case of partner companies is studied to show the interest of an Interoperability Service Utility (ISU) on business processes in a peer to peer (P2P) collaboration. Information described in the format and the ontology of a broadcasting entity is transformed by ISU into information with the format and the ontology of the receiving entity depending on the available resources of interoperation. These resources can be human operators with defined skill level or software modules of transformation in predefined languages. A design methodology of a global simulation model for estimating the impact of interoperability decision variables on performance indicators of business processes is proposed. Its implementation in an industrial case of collaboration shows its efficiency and its interest to motivate an investment in the technologies of enterprise interoperability

Ontology-based Interoperation of Linguistic Tools for an Improved Lemma Annotation in Spanish

In this paper, we present an ontology-based methodology and architecture for the comparison, assessment, combination (and, to some extent, also contrastive evaluation) of the results of different linguistic tools. More specifically, we describe an experiment aiming at the improvement of the correctness of lemma tagging for Spanish. This improvement was achieved by means of the standardisation and combination of the results of three different linguistic annotation tools (Bitext’s DataLexica, Connexor’s FDG Parser and LACELL’s POS tagger), using (1) ontologies, (2) a set of lemma tagging correction rules, determined empirically during the experiment, and (3) W3C standard languages, such as XML, RDF(S) and OWL. As we show in the results of the experiment, the interoperation of these tools by means of ontologies and the correction rules applied in the experiment improved significantly the quality of the resulting lemma tagging (when compared to the separate lemma tagging performed by each of the tools that we made interoperate)

Applications Of Machine Learning In Biology And Medicine

Machine learning as a field is defined to be the set of computational algorithms that improve their performance by assimilating data. As such, the field as a whole has found applications in many diverse disciplines from robotics and communication in engineering to economics and finance, and also biology and medicine. It should not come as a surprise that many popular methods in use today have completely different origins. Despite this heterogeneity, different methods can be divided into standard tasks, such as supervised, unsupervised, semi-supervised and reinforcement learning. Although machine learning as a field can be formalized as methods trying to solve certain standard tasks, applying these tasks on datasets from different fields comes with certain caveats, and sometimes is fraught with challenges. In this thesis, we develop general procedures and novel solutions, dealing with practical problems that arise when modeling biological and medical data. Cost sensitive learning is an important area of research in machine learning which addresses the widespread and practical problem of dealing with different costs during the learning and deployment of classification algorithms. In many applications such as credit fraud detection, network intrusion and specifically medical diagnosis domains, prior class distributions are highly skewed, which makes the training examples very much unbalanced. Combining this with uneven misclassification costs renders standard machine learning approaches useless in learning an acceptable decision function. We experimentally show the benefits and shortcomings of various methods that convert cost blind learning algorithms to cost sensitive ones. Using the results and best practices found for cost sensitive learning, we design and develop a machine learning approach to ontology mapping. Next, we present a novel approach to deal with uncertainty in classification when costs are unknown or otherwise hard to assign. Support Vector Machines (SVM) are considered to be among the most successful approaches for classification. However prediction of instances near the decision boundary depends more on the specific parameter selection or noise in data, rather than a clear difference in features. In many applications such as medical diagnosis, these regions should be labeled as uncertain rather than assigned to any particular class. Furthermore, instances may belong to novel disease subtypes that are not from any previously known class. In such applications, declining to make a prediction could be beneficial when more powerful but expensive tests are available. We develop a novel approach for optimal selection of the threshold and show its successful application on three biological and medical datasets. The last part of this thesis provides novel solutions for handling high dimensional data. Although high-dimensional data is ubiquitously found in many disciplines, current life science research almost always involves high-dimensional genomics/proteomics data. The ``omics\u27\u27 data provide a wealth of information and have changed the research landscape in biology and medicine. However, these data are plagued with noise, redundancy and collinearity, which makes the discovery process very difficult and costly. Any method that can accurately detect irrelevant and noisy variables in omics data would be highly valuable. We present Robust Feature Selection (RFS), a randomized feature selection approach dedicated to low-sample high-dimensional data. RFS combines an embedded feature selection method with a randomization procedure for stability. Recent advances in sparse recovery and estimation methods have provided efficient and asymptotically consistent feature selection algorithms. However, these methods lack finite sample error control due to instability. Furthermore, the chances of correct recovery diminish with more collinearity among features. To overcome these difficulties, RFS uses a randomization procedure to provide an accurate and stable feature selection method. We thoroughly evaluate RFS by comparing it to a number of popular univariate and multivariate feature selection methods and show marked prediction accuracy improvement of a diagnostic signature, while preserving a good stability

XML for Domain Viewpoints

Within research institutions like CERN (European Organization for Nuclear Research) there are often disparate databases (different in format, type and structure) that users need to access in a domain-specific manner. Users may want to access a simple unit of information without having to understand detail of the underlying schema or they may want to access the same information from several different sources. It is neither desirable nor feasible to require users to have knowledge of these schemas. Instead it would be advantageous if a user could query these sources using his or her own domain models and abstractions of the data. This paper describes the basis of an XML (eXtended Markup Language) framework that provides this functionality and is currently being developed at CERN. The goal of the first prototype was to explore the possibilities of XML for data integration and model management. It shows how XML can be used to integrate data sources. The framework is not only applicable to CERN data sources but other environments too.Comment: 9 pages, 6 figures, conference report from SCI'2001 Multiconference on Systemics & Informatics, Florid

Applicability of semi-supervised learning assumptions for gene ontology terms prediction

Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species. Predicting GO terms is an essential task for bioinformatics, but the number of available labelled proteins is in several cases insufficient for training reliable machine learning classifiers. Semi-supervised learning methods arise as a powerful solution that explodes the information contained in unlabelled data in order to improve the estimations of traditional supervised approaches. However, semi-supervised learning methods have to make strong assumptions about the nature of the training data and thus, the performance of the predictor is highly dependent on these assumptions. This paper presents an analysis of the applicability of semi-supervised learning assumptions over the specific task of GO terms prediction, focused on providing judgment elements that allow choosing the most suitable tools for specific GO terms. The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption. Besides, it is experimentally demonstrated that cluster and manifold assumptions are complimentary to each other and an analysis of which GO terms can be more prone to be correctly predicted with each assumption, is provided.Postprint (published version