Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

ABSTRACT: Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomar

SUBMITTER: Lai YH 

PROVIDER: S-EPMC7069964 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature