Patterns: shinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning (Chen lab, et al)
- shinyDeepDR is a web tool for predicting responses to 265 anti-cancer compounds
- It is applicable for researching both cancer cell lines and tumors
- Its interactive web interface facilitates result interpretation and investigation
- It identifies promising targets for an “undruggable” mutation in liver cancer
The bigger picture
Understanding how different genomic attributes affect drug responses in cancer is crucial for personalized oncology. Deep learning, an advanced computational method, has demonstrated significant potential in identifying and predicting these intricate interactions. One such example is the DeepDR model, which predicts how cancer cells respond to drugs. However, not all researchers have the computational resources and programming expertise to leverage this potential. Here, we introduce shinyDeepDR to bridge this gap by providing an intuitive and user-friendly web platform to access DeepDR. In the broader scope, we envision that tools like shinyDeepDR will advance cancer research by making sophisticated computational models more FAIR (findable, accessible, interoperable, and reusable).
Since 2004, UT Health San Antonio, Greehey Children’s Cancer Research Institute’s (Greehey CCRI) mission has been to advance scientific knowledge relevant to childhood cancer, contribute to understanding its causes, and accelerate the translation of knowledge into novel therapies. Greehey CCRI strives to have a national and global impact on childhood cancer by discovering, developing, and disseminating new scientific knowledge. Our mission consists of three key areas — research, clinical, and education.
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