A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy.

ABSTRACT: Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previou

SUBMITTER: He QE 

PROVIDER: S-EPMC7225787 | biostudies-literature | 2020 Jan-Dec

REPOSITORIES: biostudies-literature