Literature DB >> 16502467

Feature extraction in the analysis of proteomic mass spectra.

Xuena Wang1, Wei Zhu, Kith Pradhan, Chen Ji, Yeming Ma, Oliver John Semmes, James Glimm, Joseph Mitchell.   

Abstract

Feature extraction or biomarker selection is a critical step in disease diagnosis and knowledge discovery based on protein MS. Many studies have discussed the classification methods applied in proteomics; however, few could be found to address feature extraction in detail. In this paper, we developed a systematic approach for the extraction of mass spectrum peak apex and peak area with special emphasis on noise filtration and peak calibration. Application to a head and neck cancer data generated at the Eastern Virginia Medical School [Wadsworth, J. T., Somers, K. D., Cazares, L. H., Malik, G. et al.., Clin. Cancer Res. 2004, 10, 1625-1632] revealed that the new feature extraction method would yield consistent and highly discriminatory biomarkers.

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Year:  2006        PMID: 16502467     DOI: 10.1002/pmic.200500459

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  6 in total

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2.  LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise.

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4.  Discrimination of Tilletia controversa from the T. caries/T. laevis complex by MALDI-TOF MS analysis of teliospores.

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Journal:  Appl Microbiol Biotechnol       Date:  2022-01-17       Impact factor: 4.813

5.  Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model.

Authors:  Yuan Wang; Xiaobo Zhou; Honghui Wang; King Li; Lixiu Yao; Stephen T C Wong
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

6.  Issues and applications in label-free quantitative mass spectrometry.

Authors:  Xianyin Lai; Lianshui Wang; Frank A Witzmann
Journal:  Int J Proteomics       Date:  2013-01-16
  6 in total

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