Building a normative decision support system for clinical and operational risk management in hemodialysis.

This paper describes the design and implementation of a decision support system for risk management in hemodialysis (HD) departments. The proposed system exploits a domain ontology to formalize the problem as a Bayesian network. It also relies on a software tool, able to automatically collect HD data, to learn the network conditional probabilities.

Epigenetic analysis of the critical region I for premature ovarian failure: demonstration of a highly heterochromatic domain on the long arm of the mammalian X chromosome.

Background: X chromosome rearrangements defined a critical region for premature ovarian failure (POF) that extended for >15 Mb in Xq. It has been shown previously that the region could be divided into two functionally distinct portions and suggested that balanced translocations interrupting its proximal part, critical region 1 (CR1), could be responsible for POF through downregulation of ovary expressed autosomal genes translocated to the X chromosome.

Results And Conclusion: This study reports that such position effect can indeed be demonstrated by analysis of breakpoint regions in somatic cells of POF patients and by the finding that CR1 has a highly heterochromatic organisation, very different from that of the euchromatic autosomal regions involved in the rearrangements.

Genetic association studies for gene expressions: permutation-based mutual information in a comparison with standard ANOVA and as a novel approach for feature selection.

Mutual information (MI) is a robust nonparametric statistical approach for identifying associations between genotypes and gene expression levels. Using the data of Problem 1 provided for the Genetic Analysis Workshop 15, we first compared a quantitative MI (Tsalenko et al. 2006 J Bioinform Comput Biol 4:259-4) with the standard analysis of variance (ANOVA) and the nonparametric Kruskal-Wallis (KW) test.

How to improve fuzzy-neural system modeling by means of qualitative simulation.

The main problem in efficiently building robust fuzzy-neural models of nonlinear systems lies in the difficulty to define a "meaningful" fuzzy rule-base. Our approach to the solution of such a problem is based on a hybrid method which integrates fuzzy systems with qualitative models. We introduce qualitative models to exploit the available, although incomplete, a priori physical knowledge on the system with the goal to infer, through qualitative simulation, all of its possible behaviors.

Telemedicine and diabetes management: current challenges and future research directions.

Telemedicine is lying between fading and future. Several clinical studies and critical reviews have been published recently, but the results are inconclusive and the adoption of telemedicine interventions in clinical practice is slow. This article discusses some of the current problems related to the adoption of telemedicine systems and focuses on the information technology solutions that appear to be most promising for diabetes management in the near future.

TimeClust: a clustering tool for gene expression time series.

Unlabelled: TimeClust is a user-friendly software package to cluster genes according to their temporal expression profiles. It can be conveniently used to analyze data obtained from DNA microarray time-course experiments. It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps.

A dynamic query system for supporting phenotype mining in genetic studies.

This paper describes an information technology infrastructure aimed at supporting translational bioinformatics studies that require joint management of phenotypic and genotypic data. In particular, we integrated an electronic medical record with an open-source environment for data mining to create a flexible and easy to use query system aimed at supporting the discovery of the most frequent complex traits. We propose a logical formalization to define the phenotypes of interest; this is translated into a graphical interface that allows the user to combine different conditions relative to the electronic medical record data (e.

Automatic pedigree reconstruction for genetic studies in isolated populations.

This paper describes a tool implemented to automatically reconstruct the pedigree of an isolated population of Northern Italy with the aim of supporting genetic studies. The goal of such studies is to analyze genealogic, clinical and genetic data for genetic dissection of complex diseases. In this context the reconstruction of the population pedigree is fundamental to verify that such population is a genetic isolate and obtain the parental relationships among the individuals participating to the study.

Inferring gene regulatory networks by integrating static and dynamic data.

Objectives: The purpose of the paper is to propose a methodology for learning gene regulatory networks from DNA microarray data based on the integration of different data and knowledge sources. We applied our method to Saccharomyces cerevisiae experiments, focusing our attention on cell cycle regulatory mechanisms. We exploited data from deletion mutant experiments (static data), gene expression time series (dynamic data) and the knowledge encoded in the Gene Ontology.

Going mobile with a multiaccess service for the management of diabetic patients.

Background: Diabetes mellitus is one of the chronic diseases exploiting the largest number of telemedicine systems. Our research group has been involved since 1996 in two projects funded by the European Union proposing innovative architectures and services according to the best current medical practices and advances in the information technology area.

Method: We propose an enhanced architecture for telemedicine giving rise to a multitier application.

Temporal abstraction for feature extraction: a comparative case study in prediction from intensive care monitoring data.

Objectives: To compare two temporal abstraction procedures for the extraction of meta features from monitoring data. Feature extraction prior to predictive modeling is a common strategy in prediction from temporal data. A fundamental dilemma in this strategy, however, is the extent to which the extraction should be guided by domain knowledge, and to which extent it should be guided by the available data.

Towards knowledge-based gene expression data mining.

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks.

Precedence Temporal Networks to represent temporal relationships in gene expression data.

The reconstruction of gene regulatory networks from gene expression time series is nowadays an interesting research challenge. A key problem in this kind of analysis is the automated extraction of precedence and synchronization between interesting patterns assumed by genes over time. The present work introduces Precedence Temporal Networks (PTN), a novel method to extract and visualize temporal relationships between genes.

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks.

Background: Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed.

Burnout in health care providers of dialysis service in Northern Italy--a multicentre study.

Background: Few data are available regarding the prevalence of burnout among dialysis health care workers. Aims of the present study were to assess and compare burnout levels in a sample of nurses and physicians working in dialysis units, and to investigate their relationships with quality of life, in a cross-sectional observational study.

Methods: A total of 344 workers from 10 dialysis centres in Northern Italy completed a battery of questionnaires including the Maslach Burnout Inventory, the MOS-36 Item Short Form Health Survey [SF36: physical (PCS) and mental (MCS) component scores] and the 30-item General Health Questionnaire (GHQ30).

Diagnostic value of PRND gene expression profiles in astrocytomas: relationship to tumor grades of malignancy.

Doppel (PRND) is a paralogue of the mammalian prion (PRNP) gene. It is abundant in testis and, unlike PRNP, it is expressed at low levels in the adult central nervous system (CNS). Besides, doppel overexpression correlates with some prion-disease pathological features, such as ataxia and death of cerebellar neurons.

A new approach for the analysis of mass spectrometry data for biomarker discovery.

In the last few years a growing interest has been devoted to disease diagnosis based on proteomic profiles of body fluids generated by mass spectrometry. In this work, we will present a new approach for their analysis for biomarker discovery. In particular, we will describe a new strategy for the analysis of SELDI/MALDI-TOF serum data based on the following three steps: i) data-preprocessing, ii) feature (mass/charge ratio, m/z) reduction and selection, iii) association of the selected features to a list of compatible known proteins.

Predictive data mining in clinical medicine: current issues and guidelines.

Background: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments.

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.

Background: Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example of biological uncertainty which should be taken into account when molecular markers are used in decision making. Tissue Microarray (TMA) experiments allow for large scale profiling of tissue biopsies, investigating protein patterns characterizing specific disease states.

Inferring gene expression networks via static and dynamic data integration.

This paper presents a novel approach for the extraction of gene regulatory networks from DNA microarray data. The approach is characterized by the integration of data coming from static and dynamic experiments, exploiting also prior knowledge on the biological process under analysis. A starting network topology is built by analyzing gene expression data measured during knockout experiments.

A proposed semantic framework for diabetes education content management, customisation and delivery within the M2DM project.

M2DM (multi access services for telematic management of diabetes mellitus, ) is an EU-funded telemedicine project that aims at increasing the quality of diabetes care by improving communication between patients and caregivers. As part of this project, we have undertaken the initial work of describing the necessary requirements (framework) of an advanced educational component for M2DM in accordance with the latest Semantic Web concepts. This paper describes our proposed semantic framework for educational content management, customisation and delivery.

Comparison of two temporal abstraction procedures: a case study in prediction from monitoring data.

This paper presents an empirical comparison of two temporal abstraction procedures, that were applied to derive predictive features for a prediction problem in intensive care medicine. The first procedure employs knowledge from practitioners to derive qualitative patterns of state changes; the second procedure searches through a large number of data summaries to discover those that have predictive value. The derived features were used to predict whether postsurgical patients would need mechanical ventilation longer then 24h.

A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring.

Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro- and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL measurements is one of the most important tasks in order to assess the glucose metabolic control and to revise the therapeutic protocol.

The M2DM Project--the experience of two Italian clinical sites with clinical evaluation of a multi-access service for the management of diabetes mellitus patients.

Objectives: This paper presents a multi-access service for the management of diabetes mellitus patients and the results of its assessment in two Italian clinical sites.

Methods: The service was evaluated for one year in order to prove the advantages of these kind of systems from different points of view. In this paper the clinical, usability and technical outcomes are presented.