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

Li-Ching      Wu; Ping-Heng      Hsieh; Jorng-Tzong      Horng; Yu-Jen      Jou; Chia-Der      Lin; Kuang-Fu      Cheng; Cheng-Wen      Lin; Shih-Yin      Chen

doi:10.2174/092986612798472929

Abstract

Mass spectrometry biomarker discovery may assist patients 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.

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