Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

Prepare prescription data (such as from the Clinical Practice Research Datalink) into an analysis-ready format, with start and stop dates for each patient's prescriptions. Based on Pye et al (2018) <doi:10.1002/pds.4440>.

A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. 
    Provides functionality for automated data processing, metadata extraction, and statistical 
    analysis of imaging results. The methodology implemented in this package is based on 
    Labrosse et al. (2024) <doi:10.1016/j.xpro.2024.103274> "Protocol for quantifying drug 
    sensitivity in 3D patient-derived ovarian cancer models", which describes the complete 
    workflow for drug sensitivity analysis in patient-derived cancer models.

A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity  in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs.

Summarise patient-level drug utilisation cohorts using data mapped 
    to the Observational Medical Outcomes Partnership (OMOP) common data model. 
    New users and prevalent users cohorts can be generated and their 
    characteristics, indication and drug use summarised.

Includes various functions for playing drum sounds. beat() plays a drum sound
  from one of the six included drum kits. tempo() sets spacing between calls to beat()
  in bpm. Together the two functions can be used to create many different drum patterns.

A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.