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| Verfasst von: | Bitto, Verena [VerfasserIn]  |
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| | Hönscheid, Pia [VerfasserIn] |
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| | Besso, María José [VerfasserIn] |
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| | Sperling, Christian [VerfasserIn] |
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| | Kurth, Ina [VerfasserIn] |
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| | Baumann, Michael [VerfasserIn] |
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| | Brors, Benedikt [VerfasserIn] |
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| Titel: | Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors |
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| Verf.angabe: | Verena Bitto, Pia Hönscheid, María José Besso, Christian Sperling, Ina Kurth, Michael Baumann, Benedikt Brors |
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| E-Jahr: | 2024 |
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| Jahr: | 27 May 2024 |
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| Umfang: | 13 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 11.06.2025 |
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| Titel Quelle: | Enthalten in: npj Systems biology and applications |
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| Ort Quelle: | London : Nature Publ. Group, 2015 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 10(2024), 1, Artikel-ID 57, Seite 1-13 |
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| ISSN Quelle: | 2056-7189 |
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| Abstract: | Mass spectrometry imaging (MSI) allows to study cancer’s intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly f num low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery. |
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| DOI: | doi:10.1038/s41540-024-00385-x |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1038/s41540-024-00385-x |
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| | Volltext: https://www.nature.com/articles/s41540-024-00385-x |
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| | DOI: https://doi.org/10.1038/s41540-024-00385-x |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Cancer |
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| | Computational biology and bioinformatics |
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| K10plus-PPN: | 1927940559 |
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| Verknüpfungen: | → Zeitschrift |
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Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors / Bitto, Verena [VerfasserIn]; 27 May 2024 (Online-Ressource)
