A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data

NCU Institutional Repository > 生醫理工學院 > 系統生物與生物資訊研究所 > 期刊論文 > Item 987654321/51064

請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/51064

題名:	A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data
作者:	Wu,LC;Chen,HH;Horng,JT;Lin,C;Huang,NE;Cheng,YC;Cheng,KF
貢獻者:	系統生物與生物資訊研究所
關鍵詞:	WAVELET TRANSFORM;TIME-SERIES;SPECTRA;SERUM;CLASSIFICATION
日期:	2010
上傳時間:	2012-03-27 18:20:19 (UTC+8)
出版者:	國立中央大學
摘要:	Motivation: Mass spectrometry is a high throughput, fast, and accurate method of protein analysis. Using the peaks detected in spectra, we can compare a normal group with a disease group. However, the spectrum is complicated by scale shifting and is also full of noise. Such shifting makes the spectra non-stationary and need to align before comparison. Consequently, the preprocessing of the mass data plays an important role during the analysis process. Noises in mass spectrometry data come in lots of different aspects and frequencies. A powerful data preprocessing method is needed for removing large amount of noises in mass spectrometry data. Results: Hilbert-Huang Transformation is a non-stationary transformation used in signal processing. We provide a novel algorithm for preprocessing that can deal with MALDI and SELDI spectra. We use the Hilbert-Huang Transformation to decompose the spectrum and filter-out the very high frequencies and very low frequencies signal. We think the noise in mass spectrometry comes from many sources and some of the noises can be removed by analysis of signal frequence domain. Since the protein in the spectrum is expected to be a unique peak, its frequence domain should be in the middle part of frequence domain and will not be removed. The results show that HHT, when used for preprocessing, is generally better than other preprocessing methods. The approach not only is able to detect peaks successfully, but HHT has the advantage of denoising spectra efficiently, especially when the data is complex. The drawback of HHT is that this approach takes much longer for the processing than the wavlet and traditional methods. However, the processing time is still manageable and is worth the wait to obtain high quality data.
關聯:	PLOS ONE
顯示於類別:	[系統生物與生物資訊研究所] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	626	檢視/開啟

在NCUIR中所有的資料項目都受到原著作權保護.

社群 sharing

資料載入中.....