drug_response_w12

<p>A matrix containing drug response values (IC50 values) from patients retrieved from waves 1 and 2. Used as a toy example in <code>trainTree</code> and <code>predictTree</code> and <code>niceTree</code></p>

datasets

Implements a tree-based method specifically designed for personalized medicine applications. By using genomic and mutational data, 'ODT' efficiently identifies optimal drug recommendations tailored to individual patient profiles. The 'ODT' algorithm constructs decision trees that bifurcate at each node, selecting the most relevant markers (discrete or continuous) and corresponding treatments, thus ensuring that recommendations are both personalized and statistically robust. This iterative approach enhances therapeutic decision-making by refining treatment suggestions until a predefined group size is achieved. Moreover, the simplicity and interpretability of the resulting trees make the method accessible to healthcare professionals. Includes functions for training the decision tree, making predictions on new samples or patients, and visualizing the resulting tree. For detailed insights into the methodology, please refer to Gimeno et al. (2023) <doi:10.1093/bib/bbad200>.

Katyna Sada Del Real

Optimal Decision Trees Algorithm

Maddi Eceiza

Lucia Ruiz

Angel Rubio

drug_response_w12 function

<p>The format is:
  num [1:247, 1:119] 2.710983 2.8755433 3.4390103 2.6527257...</p>

drug_response_w12: drug_response_w12 data

Description

Usage

Arguments

Format

Examples