A Supervised LightGBM-Based Approach to the GSK.ai CausalBench Challenge (ICLR 2023)

Kaiwen Deng, Yuanfang Guan

Abstract: In this challenge, we transformed the task of detecting gene pairs with causal relationships into a supervised learning problem. We constructed a dataset for all gene pairs, with initial labels determined by gene expression correlations. A LightGBM model was trained and applied to the same data for prediction. The top 1001 pairs with the highest prediction scores were selected. In local experiments, this solution achieved a 0.3779 AUC score in the RPE1 data and a 0.3265 score in the K562 data.