Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/71186
標題: Improved Candidate Biomarker Detection Based on Mass Spectrometry Data Using the Hilbert-Huang Transform
作者: Wu, L.C.
Hsieh, P.H.
Horng, J.T.
Jou, Y.J.
Lin, C.D.
Cheng, K.F.
Lin, C.W.
Chen, S.Y.
關鍵字: Biomarker discovery
hilbert-huang transform
MALDI-TOF
mass
spectrometry
SELDI-TOF
seldi-tof ms
salivary biomarkers
identification
discovery
proteomics
alignment
serum
algorithms
cancer
期刊/報告no:: Protein and Peptide Letters, Volume 19, Issue 1, Page(s) 120-129.
摘要: Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method called Enhance HHTMass (E-HHTMass) for MALDI-TOF and SELDI-TOF mass spectrometry data which improved old HHTMass method by removing the interpolation and the biomarker discovery process. E-HHTMass integrates the preprocessing and classification functions to identify significant peaks. The results show that most known biomarker can be found and high peak appearance rate achieved comparing to MSCAP and old HHTMass2. E-HHTMass is able to adapt to spectra with a small increasing interval. In addition, new peaks are detected which can be potential biomarker after further validation.
URI: http://hdl.handle.net/11455/71186
ISSN: 0929-8665
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