Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection.

ABSTRACT:

Background

Lung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.

Result

Here we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically.

SUBMITTER: Fan X 

PROVIDER: S-EPMC6509866 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature