i2b2 to Optimize Patients Enrollment.

i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study.

An Extension of the i2b2 Data Warehouse to Support REDCap Dynamic Data Pull.

i2b2 and REDCap are two widely adopted solutions respectively to facilitate data re-use for research purpose and to manage non-for-profit research studies. REDCap provides the design specifications to build a web service used to import data from an external source with a procedure called DDP. In this work we have developed a web service that implements these specifications in order to import data from i2b2.

Automatic Processing of Anatomic Pathology Reports in the Italian Language to Enhance the Reuse of Clinical Data.

Medical reports often contain a lot of relevant information in the form of free text. To reuse these unstructured texts for biomedical research, it is important to extract structured data from them. In this work, we adapted a previously developed information extraction system to the oncology domain, to process a set of anatomic pathology reports in the Italian language.

Clinical decision support systems in child and adolescent psychiatry: a systematic review.

Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e.

Beyond Cohort Selection: An Analytics-Enabled i2b2.

The i2b2 software is a widely adopted solution for secondary use of clinical data for clinical research, specifically designed for cohort identification. i2b2 is still lacking functionalities for data analysis. The aim of this work is to empower the i2b2 framework enabling clinical researchers to perform statistical analyses for accelerating the process of hypothesis testing.

A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin.

Background: Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg).

Results: We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg.

A Dynamic Bayesian Network model for long-term simulation of clinical complications in type 1 diabetes.

The increasing prevalence of diabetes and its related complications is raising the need for effective methods to predict patient evolution and for stratifying cohorts in terms of risk of developing diabetes-related complications. In this paper, we present a novel approach to the simulation of a type 1 diabetes population, based on Dynamic Bayesian Networks, which combines literature knowledge with data mining of a rich longitudinal cohort of type 1 diabetes patients, the DCCT/EDIC study. In particular, in our approach we simulate the patient health state and complications through discretized variables.

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes.

Aims/hypothesis: We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes.

Methods: In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models.

PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification.

Phosphorylation is a protein posttranslational modification. It is responsible of the activation/inactivation of disease-related pathways, thanks to its role of "molecular switch." The study of phosphorylated proteins becomes a key point for the proteomic analyses focused on the identification of diagnostic/therapeutic targets.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification.

Aims/hypothesis: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients.

Hierarchical Naive Bayes for genetic association studies.

Background: Genome Wide Association Studies represent powerful approaches that aim at disentangling the genetic and molecular mechanisms underlying complex traits. The usual "one-SNP-at-the-time" testing strategy cannot capture the multi-factorial nature of this kind of disorders. We propose a Hierarchical Naïve Bayes classification model for taking into account associations in SNPs data characterized by Linkage Disequilibrium.

Prediction of peptide reactivity with human IVIg through a knowledge-based approach.

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction).

The two tryptophans of β2-microglobulin have distinct roles in function and folding and might represent two independent responses to evolutionary pressure.

Background: We have recently discovered that the two tryptophans of human β2-microglobulin have distinctive roles within the structure and function of the protein. Deeply buried in the core, Trp95 is essential for folding stability, whereas Trp60, which is solvent-exposed, plays a crucial role in promoting the binding of β2-microglobulin to the heavy chain of the class I major histocompatibility complex (MHCI). We have previously shown that the thermodynamic disadvantage of having Trp60 exposed on the surface is counter-balanced by the perfect fit between it and a cavity within the MHCI heavy chain that contributes significantly to the functional stabilization of the MHCI.

Accurate peak list extraction from proteomic mass spectra for identification and profiling studies.

Background: Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which contains the mass, the charge and the intensity of every detected biomolecule.

Development of a novel bioinformatics tool for in silico validation of protein interactions.

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions.

Novel topological descriptors for analyzing biological networks.

Background: Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g.

A large scale analysis of information-theoretic network complexity measures using chemical structures.

This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design. Numerous such measures have been developed so far but many of them lack a meaningful interpretation, e.g.

A Perl procedure for protein identification by Peptide Mass Fingerprinting.

Background: One of the topics of major interest in proteomics is protein identification. Protein identification can be achieved by analyzing the mass spectrum of a protein sample through different approaches. One of them, called Peptide Mass Fingerprinting (PMF), combines mass spectrometry (MS) data with searching strategies in a suitable database of known protein to provide a list of candidate proteins ranked by a score.