In this seminar, we will introduce an innovative rank-based regression algorithm that is under development in our lab. This method offers a robust alternative to existing approaches for predicting continuous clinical variables from noisy omics datasets while simultaneously identifying informative biomarkers. Our algorithm has demonstrated promising results in in-silico studies, effectively handling the high-dimensionality and noise typical of omics data. We are now seeking collaborations to explore new applications across various biomedical domains, aiming to advance personalized medicine and biomarker discovery.