MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data

MultiOmicsAgent (MOAgent) is an innovative, Python based open-source tool for biomarker discovery, utilizing machine learning techniques specifically extreme gradient-boosted decision trees to process multi-omics data. With its cross-platform compatibility, user-oriented graphical interface and a well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgent’s guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts and makes advanced data analysis accessible and reliable for a wide audience. Biographical Note Jens Settelmeier, Julia Boshart, Martin Gesell are Ph.D. candidates, Jianbo Fu, Sebastian N. Steiner are Post Doc candidates and Sandra Goetze, Patrick Pedrioli senior scientists at the Institute of Translational Medicine at Health Sciences and Technology department at ETH Zürich, Switzerland, within Professor Bernd Wollscheid’s research group who has been working in the fields of bioinformatics, clinical multi-omics with a focus on spatial cell surface proteomics. Peter J. Schüffler is professor at the institute of Pathology at the TU Munich, Germany and has been working in the field of digital pathology and clinical multi-modal studies. Diyora Salimova is junior professor at the department of Applied Mathematics at the Albert-Ludwigs-University of Freibug, Germany and has been working in the field of stochastic processes, approximation theory and machine learning related topics. Key Points MOAgent enables a guided biomarker-candidate discovery in multi-omics studies, providing a graphical interface and well-documented API. A user can run MOAgent on a personal computer without the requirement of coding a single line. MOAgent is a Python-based solution for biomarker-candidate discovery, using machine learning to analyze multi-omics data. MOAgent can address challenges like data incompleteness and class imbalances, ensuring reliable analysis. MOAgent makes advanced data analysis accessible, enhancing insights from clinical data.