Mixup SVM Learning for Compound Toxicity Prediction Using Human Pluripotent Stem Cells

Abstract

Drug discovery, characterized by its time-consuming and costly nature, demands approximately 9 to 17 years and around two billion dollars for development. Despite the extensive investment, about 90% of drugs entering clinical trials face withdrawal, with compound toxicity accounting for 30% of these instances. Ethical concerns and the discrepancy in mechanisms between humans and animals have prompted regulatory restrictions on traditional animal-based toxicity prediction methods. In response, human pluripotent stem cell-based approaches have emerged as an alternative. This paper investigates the scalability challenges inherent in utilizing pluripotent stem cells due to the costly nature of RNAseq and the lack of standardized protocols. To address these challenges, we propose applying Mixup data augmentation, a successful technique in deep learning, to kernel SVM, facilitated by Stochastic Dual Coordinate Ascent (SDCA). Our novel approach, Exact SDCA, leverages intermediate class labels generated through Mixup, offering advancements in both efficiency and effectiveness over conventional methods. Numerical experiments reveal that Exact SDCA outperforms Approximate SDCA and SGD in attaining optimal solutions with significantly fewer epochs. Real data experiments further demonstrate the efficacy of multiplexing gene networks and applying Mixup in toxicity prediction using pluripotent stem cells.