Mining whole-sample mass spectrometry proteomics data for biomarkers - An overview

Expert Systems with Applications

Volumn 36, Issue 3 PART 1, 2009, Pages 5333-5340

  McDonald, Ross A ^a   Skipp, Paul ^a   Bennell, Julia ^a   Potts, Chris ^a   Thomas, Lyn ^a   O'Connor, C David ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Biomarkers; Data mining; Mass spectrometry; Proteomics

Indexed keywords

DATA MINING; DIAGNOSIS; MAMMALS; MASS SPECTROMETRY; MOLECULAR BIOLOGY; PROTEINS;

BIOLOGICAL CHANGES; BOVINE TUBERCULOSIS; CLINICAL SAMPLES; DISEASE DIAGNOSIS; PRIMARY SCREENING; PROCESSING STEPS; PROTEOMICS; SAMPLE COLLECTION;

BIOMARKERS;

EID: 58749115234     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.06.133     Document Type: Article

References (16)

1. Arneberg R., Rajalahti T., Flikka K., Berven F.S., Kroksveen A.C., Berie M., et al. Pretreatment of mass spectral profiles: Application to proteomic data. Analytical Chemistry 79 (2007) 7014-7026
2. Conrads T.P., Fusaro V.A., Ross S., Johann D., Rajapakse V., Hitt B.A., et al. High-resolution serum proteomic features for ovarian cancer detection. Endocrine-Related Cancer 11 (2004) 163-178
3. Hu J., Coombes K.R., Morris J.S., and Baggerly K.A. The importance of experimental design in protemic mass spectrometry experiments: Some cautionary tales. Briefings in Functional Genomics and Proteomics 3 (2005) 322-331
4. Jamain, A. (2004). Meta-analysis of classification methods. Ph.D. thesis, Imperial College London, UK.
5. Karp N.A., Spencer M., Lindsay H., O'Dell K., and Lilley K.S. Impact of replicate types on proteomic expression analysis. Journal of Proteome Research 4 (2005) 1867-1871
6. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint conference on artificial intelligence (IJCAI).
7. Kvalheim O.M., Brakstad F., and Liang Y.-Z. Preprocessing of analytical profiles in the presence of homoscedastic or heroscrdastic noise. Analytical Chemistry 66 (1994) 43-51
8. Lancashire L., Schmid O., Shah H., and Ball G. Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis, and principal components analysis. Bioinformatics 21 (2005) 2191-2199
9. Li J., Orlandi R., White C.N., Rosenzweig J., Zhao J., Seregni E., et al. Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry. Clinical Chemistry 51 (2005) 2229-2235
10. McDonald, R.A. (2004). Combination in supervised classification problems. Ph.D. thesis. UK: Imperial College London.
11. McDonald R.A. The mean subjective utility score, a novel metric for cost-sensitive classifier evaluation. Pattern Recognition Letters 27 (2006) 1472-1477
12. Villanueva J., Philip J., Chaparro C.A., Li Y., Toledo-Crow R., DeNoyer L., et al. Correcting common errors in identifying cancer-specific serum peptide signatures. Journal of Proteome Research 4 (2005) 1060-1072
13. Vitzthum F., Behrens F., Anderson N.L., and Shaw J.H. Proteomics: From basic research to diagnostic application. a review of requirements and needs. Journal of Proteome Research 5 (2005) 1086-1097
14. Wagner M., Naik D., and Pothen A. Protocols for disease classification from mass spectrometry data. Proteomics 3 (2004) 1692-1698
15. West-Nielsen M., Hogdall E.V., Marchiori E., Hogdall C.K., Schou C., and Heegaard N.H. Sample handling for mass spectrometric proteomic investigations of human sera. Analytical Chemistry 77 (2005) 5114-5123
16. Wong J.W., Durante C., and Cartwright H.M. Application of fast fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets. Analytical Chemistry 77 (2005) 5655-5661