Sentence examples for inference on disease from inspiring English sources

Inference on disease dynamics is typically performed using case reporting time series of symptomatic disease.

If seroepidemiology is to become a useful tool for performing inference on disease dynamics, we must understand which disease processes have the largest effects on observations made in serological studies.

A key question when performing statistical inference on disease dynamics using serological data is whether the three processes of (i) reinfection, (ii) post-infection antibody generation, and (iii) antibody waning are statistically identifiable from one another.

More investigation will help us to understand how well alterations of DNA methylation at repeat regions (and other global methylation sites) in an accessible tissue (e.g., blood) reflect processes that are occurring in other tissues or represent systemic changes allowing for inference on disease risk or prevention.

We found that when taking into account the modularity levels, the CIPHER-HIT method can significantly improve the performance of disease gene predictions, which demonstrates modularity is one of the key features for credible inference of disease genes on the phenotype-gene heterogeneous network.

This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases.

Such systems rely on the mining of MEDLINE text [9], [10] or employ a similarity-based inference of disease genes [11], [12].

Inference of disease associations was further employed as a starting point for prediction of disease genes.

This framework can be adapted to enhance inference on infectious diseases whenever asymptomatic carriers are important and multiple datasets are available.

For example, when comparing treatment for a metastatic cancer, it would be inappropriate to measure survival from date of initial diagnosis, as we would not be able to make reasonable inferences on the disease population as a whole.

The sources of differential misclassification of disease events, which was identified in the present study, could bias results and inferences in studies on disease events resulting in veterinary treatment.