Patterns

This data set was prepared for a paper entitled "Complex Network Representation of the Structure–Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity". It contains the simulation data, and intermediate data with script files to draw main figures. The simulation data was obtained and analyzed using J-OCTA V5.0 with COGNAC 9.2.2. The intermediate data was obtained from the simulation data using COGNAC 9.2.2 or python 3.7.0. The script file could be executed in python3 with some libraries to draw main figures in the paper.

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 – predicts the sample’s response to 265 approved and investigational anti-cancer compounds, and Find Sample – 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 web tool for in silico drug screening, accessible at https://shiny.crc.pitt.edu/shinydeepdr/.

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 – predicts the sample’s response to 265 approved and investigational anti-cancer compounds, and Find Sample – 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 web tool for in silico drug screening, accessible at https://shiny.crc.pitt.edu/shinydeepdr/.

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 – predicts the sample’s response to 265 approved and investigational anti-cancer compounds, and Find Sample – 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 web tool for in silico drug screening, accessible at https://shiny.crc.pitt.edu/shinydeepdr/.