# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 2 Materials and Methods  
### Paper Selection and Data Curation  
The initial literature search for papers for inclusion in the dataset was limited based on whether the papers  
* conducted GET experiments with PEFs and plasmid DNA;
* did not use additional therapeutic techniques to achieve success;
* used a pulse configuration with a maximum of two separate voltage levels;
* provided results in terms of significant difference from the control;
* conducted experiments either in vitro or in vivo.  
The data from the papers were then manually captured in a spreadsheet using the features described in Table 1.  
For training inputs, electrode configurations and electrode types were labeled based on the descriptions provided in the source papers. Electrode types were split into either penetrating or non-penetrating electrodes, where penetrating electrodes were intended to pierce the target tissue and non-penetrating electrodes were intended to rest on the surface. In vitro studies were classed as non-penetrating electrodes due to the use of cuvettes and plate electrodes in their methodologies.  
The features Specific Tissue and Tissue as well as Specific Species and Species were added to avoid overspecification of the experimental targets. The inclusion of both features allowed the learning algorithms to decide if a particular outcome was better predicted by a general species/tissue designation rather than the specific tissue or species under study (e.g., 'Tumor' vs. 'B16 Melanoma' or 'Rat' vs. 'Sprague Dawley Rat').  
It was often the case that either the voltage or the field strength was not directly reported. However, if the paper included the relevant electrode spacings, the missing value was calculated using  
\[\text{E}=\text{V}/\text{d}, \tag{1}\]  
where E is the electric field strength in V/cm, V is the applied voltage in V, and d is the electrode spacing in cm. While this does not provide an accurate representation of the field strength between electrodes, it is commonly used in the field to relate the applied voltage and expected field strengths when reported in the literature [25; 42; 43; 44].  
In the case of the monopolar electrodes [45], the method of determining the field strength directly from the parameters and geometries reported in the papers was not possible as the grounding electrode was significantly distant from the active electrode. In this case, COMSOL FEM simulations were employed to simulate the field strengths proximal to the active electrode during pulsing. Geometries and tissue types were determined from the papers, whereas the electrical properties of the tissues of interest were determined based on electrical parameters described in [16].  
as a binary variable rather than entering the outcome data from the papers directly. This binary classification allowed the models to report either a'success' or 'fail', rather than dynamically predicting gene transfection efficiencies or patient outcomes based on the goal of the study. In the case of human cancer trials, success was considered as either stable disease or a complete response, whereas failure was considered in the case of progressive disease. In all other cases, success was considered a statistically significant improvement in the biological responses between the test group and a fixed control. In the event that a particular paper had multiple reported experiments, of which only a portion conformed to the requirements for inclusion in the dataset, only the relevant experiments were included in the dataset.