Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data.

1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data.

Incremental and unifying modelling formalism for biological interaction networks.

Background: An appropriate choice of the modeling formalism from the broad range of existing ones may be crucial for efficiently describing and analyzing biological systems.

Results: We propose a new unifying and incremental formalism for the representation and modeling of biological interaction networks. This formalism allows automated translations into other formalisms, thus enabling a thorough study of the dynamic properties of a biological system.

Model of interactions in biology and application to heterogeneous network in yeast.

A major challenge for bioinformatics and theoretical biology is to build and analyse a unified model of biological knowledge resulting from high-throughput experiment data. Former work analyzed heterogeneous data (protein-protein interactions, genetic regulation, metabolism, synexpression) by modelling them by graphs. These models are unable to represent the qualitative dynamics of the reactions or to model the n-ary interactions.