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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Peng Stephen, Wong Zhou, Xiaobo Li, Houqiang Wang, Honghui |
| Abstract | Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data. |
| Starting Page | 1054 |
| Ending Page | 1066 |
| Page Count | 13 |
| File Format | |
| ISSN | 15455963 |
| DOI | 10.1109/TCBB.2009.56 |
| Volume Number | 8 |
| Issue Number | 4 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-07-01 |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Mass spectrometry, peak identification, peak tree, scale-space filtering, wavelets, feature selection. |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Biotechnology Applied Mathematics |
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