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

  • 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
  • ETH Zürich
  • Swiss Institute of Bioinformatics
  • ETH PHRT Swiss Multi-Omics Center (SMOC)
  • Albert-Ludwigs-Universität Freiburg

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)2816-2831
Number of pages16
JournalJournal of Proteome Research
Volume24
Issue number6
DOIs
StatePublished - 6 Jun 2025

Keywords

  • Python tool
  • biomarker discovery
  • extreme gradient-boosted decision trees
  • machine learning
  • multiomics

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