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

Title:
MultiOmicsAgent: Guided Extreme Gradient-Boosted Decision Trees-Based Approaches for Biomarker-Candidate Discovery in Multiomics Data.
Authors:
Settelmeier J; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland.; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland., Goetze S; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland.; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.; ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich 8093, Switzerland., Boshart J; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland., Fu J; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland.; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.; ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich 8093, Switzerland., Khoo A; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland., Steiner SN; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland., Gesell M; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland., Hammer J; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland., Schüffler PJ; Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich 81675, Germany., Salimova D; Department for Applied Mathematics, Albert-Ludwigs-University of Freiburg, Freiburg 79104, Germany., Pedrioli PGA; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland.; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.; ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich 8093, Switzerland.; Department of Biology, ETH, Zurich 8093, Switzerland., Wollscheid B; Institute of Translational Medicine at the Department of Health Sciences and Technology, ETH, Zurich 8093, Switzerland.; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.; ETH PHRT Swiss Multi-Omics Center (SMOC), Zurich 8093, Switzerland.
Source:
Journal of proteome research [J Proteome Res] 2025 Jun 06; Vol. 24 (6), pp. 2816-2831. Date of Electronic Publication: 2025 May 25.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 101128775 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1535-3907 (Electronic) Linking ISSN: 15353893 NLM ISO Abbreviation: J Proteome Res Subsets: MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c2002-
MeSH Terms:
Decision Trees* , Biomarkers*/analysis , Software* , Proteomics*/methods, Humans ; Machine Learning ; Multiomics
References:
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Contributed Indexing:
Keywords: Python tool; biomarker discovery; extreme gradient-boosted decision trees; machine learning; multiomics
Substance Nomenclature:
0 (Biomarkers)
Entry Date(s):
Date Created: 20250526 Date Completed: 20250606 Latest Revision: 20250612
Update Code:
20250612
PubMed Central ID:
PMC12150338
DOI:
10.1021/acs.jproteome.4c01066
PMID:
40415340
Database:
MEDLINE

Weitere Informationen

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.