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Autoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniques

2026-01-21Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências Biológicas
dc.contributor.authorHauns, Sven
dc.contributor.authorPinto, Frederico G.
dc.contributor.authorKhyriem, Costerwell
dc.contributor.authorSingh, Ankita
dc.contributor.authorAl-Sadi, Azzat
dc.contributor.authorYazeedi, Talal Al
dc.contributor.authorMohammad, Rasheed
dc.contributor.authorCisse, Babacar
dc.contributor.authorGarrett, Timothy J.
dc.contributor.authorUddin, Mohammed
dc.contributor.authorSoares, Nelson C.
dc.contributor.authorBackofen, Rolf
dc.contributor.authorAlkhnbashi, Omer S.
dc.date.accessioned2026-09-29T11:15:45Z
dc.date.available2026-09-29T11:15:45Z
dc.date.issued2026-01-21
dc.description.abstractAccurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved patient outcomes. Metabolomic signatures represent a robust molecular framework for elucidating cancer-associated biochemical reprogramming. The use of artificial intelligence (AI) in biology in recent years has become widespread and promising. This study introduces a novel predictive method that integrates an Autoencoder, random forest-based feature selection and Tabular Prior-data Fitted Network (TabPFN) to achieve high diagnostic accuracy from metabolomics data of prostate and BC patients. The datasets were acquired using paper spray ionization mass spectrometry and flow injection-traveling-wave ion mobility-mass spectrometry of individuals diagnosed with PC and BC. When leveraging metabolomic profiling data from two distinct sources, PC urine and serum samples, the proposed model achieved an accuracy up to 98.75% in distinguishing diseased from healthy conditions. Additionally, we employed a BC dataset containing metabolic and lipidomic signatures acquired from core needle biopsies using a miniature MS platform coupled with PSI to assess the fidelity of our implementation across distinct cancer types. Our results on a well-characterized targeted dataset show that we can effectively reduce high-dimensional data into latent feature representations. At the same time, TabPFN captures tumor progression-related changes and feature interaction, thereby enhancing the possibility that the model will be a highly potent and effective tool for stage-specific diagnostic precision. Most existing machine learning approaches for disease diagnosis primarily rely on imaging, genomics, or clinical parameters, often overlooking the critical role of metabolites in identifying disease-specific biochemical signatures. By integrating metabolite-specific data with a robust deep-learning approach, this study demonstrates the transformative potential of AI in metabolomics-based diagnostics. The proposed model offers scalability and versatility, with applications extending beyond oncology to a much broader disease profiling aspect. These findings emphasize the value of combining multi-source metabolomic data with deep learning to advance personalized medicine and enhance diagnostic efficiency in clinical practice.eng
dc.description.sponsorshipThis study was supported by research grants from Dubai Future Foundation/Dubai Research, Development and Innovation Program (RDI 2025/DRDI0406), the Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Health, Dubai, United Arab Emirates. Internal grant MBRU, Dubai Health, College of Medicine grants MBRU-CM-RG2024-07 and MBRU-CM-RG2025-12. Deutsche Forschungsgemeinschaft grant BA 2168/23, Much More Than Defence: The Multiple Functions and Facets of CRISPR–Cas; Deutsche Forschungsgemeinschaft, grant BA 2168/25-1, Einfluss von RNA-bindenden Proteinen und mRNA-Strukturen auf alternative Translation-Regulationsmechanismen im entzündlichen Tumorgeschehen. The article processing charge is funded by the Baden-Wuerttemberg Ministry of Science, Research and Art and the University of Freiburg in the funding programme Open Access Publishing. The authors acknowledge support by the High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen, the state of Baden-Württemberg through bwHPC, and the German Research Foundation (DFG) through grant number INST 37/935–1 FUGG. This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, and 031A538A).
dc.identifier.citationGigascience. 2026 Jan 21;15:giag053. doi: 10.1093/gigascience/giag053
dc.identifier.doi10.1093/gigascience/giag053
dc.identifier.eissn2047-217X
dc.identifier.pmid42096511
dc.identifier.urihttp://hdl.handle.net/10400.18/11350
dc.language.isoeng
dc.peerreviewedyes
dc.publisherOxford University Press
dc.relation.hasversionhttps://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giag053/8671966
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial Intelligence
dc.subjectAutoencoder
dc.subjectBreast Cancer
dc.subjectCancer Diagnostics
dc.subjectMetabolomics
dc.subjectPaper Spray Ionization Mass Spectrometry
dc.subjectProstate Cancer
dc.subjecttabPFN
dc.subjectGenómica Funcional e Estrutural
dc.titleAutoencoder/RandomForest-TabPFN for cross-cancer metabolomics: prostate and breast cancer diagnosis using paper spray and ion mobility-mass spectrometry techniqueseng
dc.typejournal article
dcterms.referenceshttps://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giag053/8671966#supplementary-data
dspace.entity.typePublication
oaire.citation.startPagegiag053
oaire.citation.titleGigascience
oaire.citation.volume15
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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