Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease.

Aims: ICD codes are used for classification of hospitalizations. The codes are used for administrative, financial, and research purposes. It is known, however, that errors occur.

Population age and household structures shape transmission dynamics of emerging infectious diseases: a longitudinal microsimulation approach.

Host population demographics and patterns of host-to-host interactions are important drivers of heterogeneity in infectious disease transmission. To improve our understanding of how population structures and changes therein influence disease transmission dynamics at the individual and population level, we model a dynamic age- and household-structured population using longitudinal microdata drawn from Belgian census and population registers. At different points in time, we simulate the spread of a close-contact infectious disease and vary the age profiles of infectiousness and susceptibility to reflect specific infections (e.

Mining minimal motif pair sets maximally covering interactions in a protein-protein interaction network.

Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible.

Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana.

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge.

SLIDER: a generic metaheuristic for the discovery of correlated motifs in protein-protein interaction networks.

Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network.