# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 1 Introduction
Despite this, successful GET protocols have been established in various tissues empirically, including the development of GET-mediated interleukin-12 (IL-12) delivery to melanoma with successful phase I and phase II clinical trials, indicating that plasmid DNA delivery encoding IL-12 can stimulate the immune system to clear treated lesions and metastatic disease [22; 23]. While there have been major advancements in pulse type, electrode design [24; 25; 26], external stimuli such as heat [27; 28], and real-time treatment endpoint detection using impedance [20; 29; 30], the underlying problem of searching the vast parameter space remains. Machine learning models have been in use for many years but, due to the recent increases in computing power, have found more mainstream accessibility and acceptance. Models have been applied to diagnostic image processing in radiology [31] and histological imaging [32] and in adverse event detection using respiratory data [33]. Machine learning models have also been applied to the field of electroporation, specifically artificial neural networks (ANNs) for IRE [34; 35], support vector machines (SVMs) for interpreting post-ablation outcomes from magnetic resonance imaging (MRI) [36], and fully convolutional networks (FCNs) and computer vision for the identification of potential tumors to treat using a multi-electrode structure [37]. In the field of GET, machine learning has only been used to locate cells using computer vision for the application of targeted, single-cell therapies [38] or in in vitro studies limited to single cell lines [39]. To date, the prospect of applying these techniques to GET on a tissue scale remains elusive. A complicating factor to the development and deployment of such a machine learning model is that there is no standard for the reporting of pulsing parameters across different studies and no existing repository or database for documenting pulsing parameters and resulting tissue responses. While the methodologies used in reporting are varied, the related parameters can often be translated from one lab to another, such as how some labs report the time between pulses or a pulse frequency, which both effectively describe the same thing. However, this introduces the possibility of errors in translation or misinterpretation of the intention behind the way in which these values are reported. A standardized method would avoid such potential errors and allow for the rapid creation of datasets where all necessary parameters are reported in a uniform way, avoiding the need for translation or interpretation. ANNs were the first choice for the models as they are extremely customizable and flexible when dealing with variable input data types and have been successfully employed in similar endeavors in the field of IRE [34; 35]. However, they suffer from a need for large datasets to effectively predict outcomes. This need grows exponentially as the number of features, or data categories, increases [40]. Since the data available are relatively limited and have a comparatively large number of features, SVMs were chosen as a second option as they are capable of accurate predictions with more limited sample sizes compared to ANNs [41]. This paper presents an exploratory study on the application of two machine learning models to a dataset of GET experiments published over the course of the last 30 years.