Ground settlement prediction for highway subgrades with sparse data using regression Kriging.

Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies.

Bioinformatics identification of characteristic genes of cervical cancer via an artificial neural network.

Background: Artificial neural networks (ANNs) have been extensively used in the field of medicine. The present hypothesis-free study sought to use an ANN to identify the characteristic genes of cervical cancer (CC).

Methods: RNA sequencing profiles were obtained from the GSE7410, GSE9750, GSE63514, and GSE52903 datasets.