Predicting Drug Response with Multi-Task Gradient-Boosted Trees in Epilepsy
Julia Hellmig, Christian M. Boßelmann, Roland Krause, Patrick May, Stefan Wolking, Gianpiero L. Cavalleri, Norman Delanty, John J. Craig, Chantal Depondt, Bobby P. C. Koeleman, Anthony Guy Marson, Terence John O'Brien, Josemir W. A. S. Sander, Graeme John Sills, Pasquale Striano, Federico Zara, Hreinn Stefánsson, Kari Stefansson, Sanjay M. Sisodiya, Holger Lerche · bioRxiv (Cold Spring Harbor Laboratory) · 2025
Abstract Motivation Despite the availability of numerous anti-seizure medications (ASMs), drug resistance remains a major issue for people with epilepsy. The probability of achieving seizure freedom diminishes with each unsuccessful drug trial, and the impact of genetic and clinical markers on ASM response remains unclear. To address this issue, we used state-of-the-art machine learning (ML) methods to predict the response of people with epilepsy to individual ASMs based on their clinical and genomic information. Results To overcome data sparsity for less common drugs, we implement a multi-task (MT) learning approach for gradient-boosted trees (GBTs), assuming that predicting responses to different ASMs involves similar tasks. This strategy allows models for less prevalent drugs to leverage the more abundant data available for other drugs during training. The proposed model outperforms individual and combined drug-response predictions for most drugs. Our findings identify key genomic and clinical features influencing drug response, enhancing understanding of individual drug responses in people with epilepsy, and aiding clinicians in making informed treatment decisions. Availability and Implementation Due to privacy reasons data is not publically available. The code will be made available upon acceptance under https://github.com/pfeiferAI/MT-GBT