PSR


CGMMC	PSR : Prediction of survival and recurrence of pancreatic cancer by integrating multi-omics data

News	2019/6/14: Source codes and data sets are updated.
Introduction	In this study, we can classify patients with a high probability of recurrence and a high risk of PAAD. We manually constructed the approaches to infer tumor progression. Also, we propose the factors that can be used to predict prognosis with deep learning techniques by integrating multi-omics data. Moreover, this study provides insight into new personalized therapies on the basis of patients' individual multi-omics data and mutations by identifying the genetic variants.
Source codes	Source codes implemented in R and python are available here . Instructions for running source codes are here . Required input files for each step are described in instructions.
Data sets	The pancreas adenocarcinoma (PAAD) data sets including the WXS, count of mRNA, miRNA, and methylation data used in this paper were collected from TCGA (https://cancergenome.nih.gov/). The clinical data of PAAD patients was downloded from the cBioPortal (https://www.cbioportal.org/).
Contacts	baekbini@gist.ac.kr, hyunjulee@gist.ac.kr