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

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

Volume: 19 Issue: 1

Author(s): Li-Ching Wu, Ping-Heng Hsieh, Jorng-Tzong Horng, Yu-Jen Jou, Chia-Der Lin, Kuang-Fu Cheng, Cheng-Wen Lin and Shih-Yin Chen

Affiliation:

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

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.

Cite this article as:

Wu Li-Ching, Hsieh Ping-Heng, Horng Jorng-Tzong, Jou Yu-Jen, Lin Chia-Der, Cheng Kuang-Fu, Lin Cheng-Wen and Chen Shih-Yin, Improved Candidate Biomarker Detection Based on Mass Spectrometry Data Using the Hilbert-Huang Transform, Protein & Peptide Letters 2012; 19 (1) . https://dx.doi.org/10.2174/092986612798472929