Metabolic profiling on 2D NMR TOCSY spectra using machine learning

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dc.contributor.advisorWöhler, Christian
dc.contributor.authorMigdadi, Lubaba Yousef Hazza
dc.contributor.refereeKummert, Franz
dc.date.accepted2023-06-02
dc.date.accessioned2023-11-07T07:09:29Z
dc.date.available2023-11-07T07:09:29Z
dc.date.issued2023
dc.description.abstractDue to the dynamicity of biological cells, the role of metabolic profiling in discovering biological fingerprints of diseases, and their evolution, as well as the cellular pathway of different biological or chemical stimuli is most significant. Two-dimensional nuclear magnetic resonance (2D NMR) is one of the fundamental and strong analytical instruments for metabolic profiling. Though, total correlation spectroscopy (2D NMR 1H -1H TOCSY) can be used to improve spectral overlap of 1D NMR, strong peak shift, signal overlap, spectral crowding and matrix effects in complex biological mixtures are extremely challenging in 2D NMR analysis. In this work, we introduce an automated metabolic deconvolution and assignment based on the deconvolution of 2D TOCSY of real breast cancer tissue, in addition to different differentiation pathways of adipose tissue-derived human Mesenchymal Stem cells. A major alternative to the common approaches in NMR based machine learning where images of the spectra are used as an input, our metabolic assignment is based only on the vertical and horizontal frequencies of metabolites in the 1H-1H TOCSY. One- and multi-class Kernel null foley–Sammon transform, support vector machines, polynomial classifier kernel density estimation, and support vector data description classifiers were tested in semi-supervised learning and novelty detection settings. The classifiers’ performance was evaluated by comparing the conventional human-based methodology and automatic assignments under different initial training sizes settings. The results of our novel metabolic profiling methods demonstrate its suitability, robustness, and speed in automated nontargeted NMR metabolic analysis.en
dc.identifier.urihttp://hdl.handle.net/2003/42189
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24023
dc.language.isoende
dc.subjectMachine learningen
dc.subjectNovelty detectionen
dc.subject2D NMRen
dc.subjectMetabolic profilingen
dc.subjectTOCSYen
dc.subjectMetabolomicsen
dc.subjectTwo-dimensional nuclear magnetic resonanceen
dc.subjectSemi-supervised learningen
dc.subject.ddc620
dc.subject.rswkMaschinelles Lernende
dc.subject.rswkMustererkennungde
dc.subject.rswkZweidimensionale NMR-Spektroskopiede
dc.subject.rswkMetabolomikde
dc.subject.rswkTeilĂĽberwachtes Lernende
dc.titleMetabolic profiling on 2D NMR TOCSY spectra using machine learningen
dc.typeTextde
dc.type.publicationtypePhDThesisde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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