Service-oriented computing, an emerging paradigm for architecting and implementing business collaborations within and across organizational boundaries, is currently of interest to both software vendors and scientists. While the technologies for implementing and interconnecting basic services are reaching a good level of maturity, modeling service interaction from a global viewpoint, that is, representing service choreographies, is still an open challenge. The main problem is that, although declarativeness has been identified as a key feature, several proposed approaches specify choreographies by focusing on procedural aspects, leading to over-constrained and over-specified models. To overcome these limits, we propose to adopt DecSerFlow, a truly declarative language, to model choreographies. Thanks to its declarative nature, DecSerFlow semantics can be given in terms of logic-based languages. In particular, we present how DecSerFlow can be mapped ontoLinear Temporal Logicand ontoAbductive Logic Programming. We show how the mappings onto both formalisms can be concretely exploited to address the enactment of DecSerFlow models, to enrich its expressiveness and to perform a variety of different verification tasks. We illustrate the advantages of using a declarative language in conjunction with logic-based semantics by applying our approach to a running example.

In data mining, association and correlation rules are inferred from data in order to highlight statistical dependencies among attributes. The metrics defined for evaluating these rules can be exploited to score relationships between attributes in Bayesian network learning. In this paper, we propos e two novel methods for learning Bayesian networks from data that are based on the K2 learning algorithm and that improve it by exploiting parameters normally defined for association and correlation rules. In particular, we propose the algorithms K2-Lift and K2-X2, that exploit the lift metric and the X2 metric respectively. We compare K2-Lift, K2-X2 with K2 on artificial data and on three test Bayesian networks. The experiments show that both our algorithms improve K2 with respect to the quality of the learned network. Moreover, a comparison of K2-Lift and K2-X2 with a genetic algorithm approach on two benchmark networks show superior results on one network and comparable results on the other.

MOTIVATION: Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells). RESULTS: We applied here Fun&Co to the molecular study of cardiovascular system and the identification of the specific molecular pathways in heart, skeletal and smooth muscles (using 317 microarrays) and to reveal functional differences between the three different kinds of muscle cells. AVAILABILITY: Application is online at http://tommy.unife.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This technology yields a global view of gene expression that can be used in several ways. Functional insight into expression profiles is routinely obtained by using Gene Ontology terms associated to the cellular genes. In this paper, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). By using this "functional correlations comparison" we explore all possible pairs of genes identifying the affected biological processes by analyzing in a pair-wise manner gene expression patterns and linking correlated pairs with Gene Ontology terms. RESULTS: We apply here this "functional correlations comparison" approach to identify the existing correlations in hepatocarcinoma (161 microarray experiments) and to reveal functional differences between normal liver and cancer tissues. The number of well-correlated pairs in each GO term highlights several differences in genetic interactions between cancer and normal tissues. We performed a bootstrap analysis in order to compute false detection rates (FDR) and confidence limits. CONCLUSION: Experimental results show the main advantage of the applied method: it both picks up general and specific GO terms (in particular it shows a fine resolution in the specific GO terms). The results obtained by this novel method are highly coherent with the ones proposed by other cancer biology studies. But additionally they highlight the most specific and interesting GO terms helping the biologist to focus his/her studies on the most relevant biological processes.

The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibi-otic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration.

We propose to adopt a formalism, based on social integrity constraints (ICs), for specifying social interactions between actors involved in a guideline. ICs allow us to represent interaction protocols using a logic formalism and to perform an on-the-fly verification of the protocol's application compliance, based on an abductive proof procedure which operates on relevant events occurred during its application. The paper presents the results of a first trial performed on a microbiological clinical guideline which exploits the potentialities of the formalism in representing and verifying the compliance to medical guidelines.

We describe a knowledge based system for microbiological laboratory data validation and \nbacteria infections monitoring. The knowledge base has been obtained from international \nstandard guidelines for microbiological laboratory practice, from experts’ suggestions and \nfrom data mining. In this work, we evaluate the system in terms of accuracy on a test dataset.