The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based "white-box" classification algorithm for medical data.

Background: Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay.

Methods: Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection.

PASE: a web-based platform for peptide/protein microarray experiments.

Peptide microarray technology requires bioinformatics and statistical tools to manage, store, and analyze the large amount of data produced. To address these needs, we developed a system called protein array software environment (PASE) that provides an integrated framework to manage and analyze microarray information from polypeptide chip technologies.

GGM: efficient navigation and mining in distributed genomedical data.

The integration of genomics and patient related data is considered as one of the most promising investigation topic in health care research. Started in 2004, the Grid for Geno Medicine (GGM) project aims at providing a comprehensive grid software infrastructure designed to allow biologists to mine and analyze relationships between medical, genetic, and genomic data stored in distributed datawarehouses. The proposed layered service oriented architecture offers a number of independent but compliant services that can be deployed in a grid environment.