mutations_w12

A binary matrix containing mutation values, wether the mutation is present or not in the patient 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>

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

mutations_w12 function

A binary matrix with,where there are 247 patients from the waves and 70 different mutations are considered. 
The format is:
 num [1:247, 1:70] 0 0 0 0 0 1...

mutations_w12: mutations_w12 Data Set

Description

Usage

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Format

Examples