Work

MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data

Year: 2025

Type: article

Abstract: 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 multiomics data. With its cross-platform compatibility, user-oriented graphical interface, and 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 normalization, 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, making advanced data analysis accessible and reliable for a wide audience.

Source: Journal of Proteome Research

DOI: 10.1021/acs.jproteome.4c01066

Authors: Jens Settelmeier, Sandra Goetze, Julia Boshart, Jianbo Fu, Amanda Khoo, Sebastian N. Steiner, Martin Gesell, Jacqueline Hammer, Peter J. Schüffler, Diyora Salimova, Patrick G. A. Pedrioli, Bernd Wollscheid

Institutions: SIB Swiss Institute of Bioinformatics, ETH Zurich, Institute of Translational Medicine, Technical University of Munich, University of Freiburg

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

OpenAlex ID: W4410729740

FWCI: 0.504

Cites: 23

Cited by: 2

Related to: 10