shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning

Li Ju Wang, Michael Ning, Tapsya Nayak, Michael J. Kasper, Satdarshan P. Monga, Yufei Huang, Yidong Chen, Yu Chiao Chiu

Research output: Contribution to journalArticlepeer-review

Abstract

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: “Find Drug,” which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and “Find Sample,” which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

Original languageEnglish (US)
Article number100894
JournalPatterns
Volume5
Issue number2
DOIs
StatePublished - Feb 9 2024

Keywords

  • DSML3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • R Shiny app
  • cancer
  • deep learning
  • drug response
  • prediction
  • web tool

ASJC Scopus subject areas

  • General Decision Sciences

Fingerprint

Dive into the research topics of 'shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning'. Together they form a unique fingerprint.

Cite this