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dc.contributor.authorKarakaşlar, E. Onuren_US
dc.contributor.authorCoşkun, B.en_US
dc.contributor.authorOutilaft, H.en_US
dc.contributor.authorNamer, I. J.en_US
dc.contributor.authorÇiçek, A. Ercümenten_US
dc.date.accessioned2021-02-18T11:51:02Z
dc.date.available2021-02-18T11:51:02Z
dc.date.issued2020
dc.identifier.issn1545-5963
dc.identifier.urihttp://hdl.handle.net/11693/75458
dc.description.abstract1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1H-13C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13C dimension by just performing 1H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97 percent accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TCBB.2019.2920646en_US
dc.subjectMetabolomicsen_US
dc.subjectHRMAS NMRen_US
dc.subjectHSQC NMRen_US
dc.titlePredicting carbon spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for online feedback during surgeryen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage719en_US
dc.citation.epage725en_US
dc.citation.volumeNumber17en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1109/TCBB.2019.2920646en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorKarakaşlar, E. Onur
dc.contributor.bilkentauthorÇiçek, A. Ercüment


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