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Improved Candidate Biomarker Detection Based on Mass Spectrometry Data Using the Hilbert-Huang Transform

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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.

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Keywords: Biomarker discovery; Denoising; GUI; HHTmass; Hilbert-Huang transform; MALDI-TOF; MASCAP; Mass spectrometry; Nephrological disease; Oral cancer:; SELDI-TOF; SpecAlign; Urinary hepcidin; WEKA; mass spectrometry

Document Type: Research Article

Publication date: 2012-01-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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