Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes - Alex Otten; Michael Francis; Anna Bulysheva (Applied Sciences)
Gene electrotransfer (GET) is a physical method of gene delivery to various tissues utilizing pulsed electric fields to transiently permeabilize cell membranes to allow for genetic material transfer and expression. Optimal pulsing parameters dictate gene transfer efficiency and cell survival, which are critical for the wide adaptation of GET as a gene therapy technique. Tissue heterogeneity complicates the delivery process, requiring the extensive optimization of pulsing protocols currently empirically optimized. These experiments are time-consuming and resource-intensive, requiring large numbers of animals for in vivo optimization. Advances in machine learning (ML) and computing power, data analysis, and model generation using ML techniques, such as neural networks, enable predictive modeling for GET. ML models have been used previously to predict ablation performance in irreversible electroporation procedures and single-cell electroporation platforms. In this work, we present ML predictive models that could be used to optimize pulsing parameters based on already completed experiments. The models were trained on 132 data points from 19 papers with the Matlab Statistics and Machine Learning Toolbox. An artificial neural network (ANN) was generated that could predict binary treatment outcomes with an accuracy of 71.8%. Support vector machines (SVMs) using selected features based on <semantics>χ2</semantics> tests were also explored. All models used a maximum of 24 features as input, spread across target species, needle configuration, pulsing parameters, and plasmid parameters. Pulse voltage and pulse width dominated as the critical parameters, followed by field strength, dose, and electrode with the greatest impact on GET efficiency. This study elucidates areas where predictive ML algorithms may ideally inform GET study design to accelerate optimization and improve efficiencies upon the further training of these models.