Compliance checking of cancer-screening Careflows: an approach based on Computational Logic.

Clinical guidelines and Careflow systems have been recently identified as a means to improve and standardize health care services. A number of ICT-based management solutions have been proposed, focussing on several aspects such as specification, process logs verification with respect to specification (compliance), enactment and administration of careflows. In this paper we introduce the GPROVE framework, based on Computational Logic, and focused on the (formal) specification of careflows and on the compliance verification of the process executions w.r.t. the specified models. In particular, we show its application to the Cancer Screening Guideline used by the sanitary organization of the Emilia Romagna region, discussing its formalization in GPROVE and the results of the compliance checking applied to logs of the screening process.

Fun&Co: identification of key functional differences in transcriptomes.

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.

Artificial intelligence techniques for monitoring dangerous infections.

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.

Finding biological process modifications in cancer tissues by mining gene expression correlations.

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.