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Báo cáo y học: "A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies"

Tong et al. Clinical Proteomics 2011, 8:14 Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies. | Tong et al. Clinical Proteomics 2011 8 14 http www.clinicalproteomicsjournal.eom content 8 1 14 CLINICAL PROTEOMICS RESEARCH Open Access A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis stem cell and melanoma cancer studies 1 1 r I r 1 1 2 2 1 Dong L Tong David J Boocock Clare Coveney Jaimy Saif Susana G Gomez Sergio Querol Robert Rees and Graham R Ball1 Correspondence dong.tong@ntu. ac.uk 1The John van Geest Cancer Research Centre School of Science and Technology Nottingham Trent University Clifton Lane Nottingham NG11 8NS UK Full list of author information is available at the end of the article 2 BioMed Central Abstract Introduction Raw spectral data from matrix-assisted laser desorption ionisation time-of-flight MALDI-TOF with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources melanoma serum and cord blood plasma are used in our study. Method Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions an ANN-based predictive model was constructed to examine the predictive power of these ions for classification. Results Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90 classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under