Performance of a genetic algorithm for mass spectrometry proteomics

BMC Bioinformatics

Volumn 5, Issue , 2004, Pages

  Jeffries, Neal O ^a  

^a NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM'S PERFORMANCE; CLASSIFICATION ACCURACY; CROSS VALIDATION; HEALTHY INDIVIDUALS; MASS SPECTROMETRY DATA; MODEL PERFORMANCE; MODEL SELECTION PROCEDURES; SENSITIVITY AND SPECIFICITY;

MASS SPECTROMETRY; MOLECULAR BIOLOGY;

GENETIC ALGORITHMS;

ACCURACY; ANALYTIC METHOD; ANALYTICAL ERROR; ARTICLE; CANCER PATIENT; CHROMOSOME ANALYSIS; CONTROLLED STUDY; GENE CLUSTER; GENETIC ALGORITHM; HUMAN; MAJOR CLINICAL STUDY; MASS SPECTROMETRY; PROTEOMICS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS; ALGORITHM; BIOLOGICAL MODEL; EVALUATION STUDY; GENETIC PROCEDURES; PROTEIN DATABASE; STANDARDS;

ALGORITHMS; DATABASES, PROTEIN; GENETIC TECHNIQUES; MASS SPECTROMETRY; MODELS, GENETIC; PROTEOMICS;

EID: 13244277613     PISSN: 14712105     EISSN: None     Source Type: Journal    
DOI: 10.1186/1471-2105-5-180     Document Type: Article

References (23)

1. Petricoin E, Ardekani A, Hitt B, Levine P, Fusaro V, Steinberg S, Mills G, Simone C, Fishman D, Kohn E, Liotta L: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002, 359:572-577.
2. Adam BL, Qu Y, Davis J, Ward M, Clements M, Cazares L, Semmes O, Schellhammer P, Yasui Y, Feng Z, Wright GJ: Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Research 2002, 62:3609-3614.
3. Li J, Zhang Z, Rosenzweig J, Yang Y, Chan D: Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clinical Chemistry 2002, 48(8): 296-1304.
4. Qu Y, Adam BL, Yasui Y, Ward M, Cazares L, Schellhammer P, Feng Z, Semmes O, Wright GJ: Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clinical Chemistry 2002, 48(10):1835-1843.
5. Sorace J, Zhan M: A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 2003, 4:24 [http://www.biomedcentral.com/1471-2105/4/24].
6. Qu Y, Adam B, Thornquist M, Potter J, Thompson M, Yasui Y, Davis J, Schellhammer P, Cazares L, Clements M, Wright G, Feng Z: Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data. Biometrics 2003, 59: 43-151.
7. Baggerly K, Morris J, Wang J, Gold D, Xiao L, Coombes K: A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteornics spectra from serum samples. Proteomics 2003, 3(9):1667-1672.
8. Petricoin EF, Ornstein DK, Paweletz C, Ardekani A, Hackett P, Hitt B, Velassco A, Trucco C, Wiegand L, Wood K, Simone C, Levine P, Linehan W, Emniert-Buck M, Steinberg W, Kohn E, Liotta L: Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute 2002, 94(20):1576-1578.
9. Petricoin EF, Liotta LA: SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Current Opinion in Biotechnology 2004, 15:24-30.
10. Link to NCI-FDA Clinical Proteomics Databank [http://krisa.ninds.nih.gov/gaweb/links/]
11. Reeves C, Rowe J: Genetic Algorithms : Principles and Perspectives : a Guide to GA Theory Boston: Kluwer Academic; 2003.
12. Conrads T, Fusaro V, Ross S, Johann D, Rajapakse V, Hitt B, Steinberg S, Kohn E, Fishman D, Whiteley G, Barrett J, Liotta L III EP, Veenstra T: High-resolution serum proteomic features for ovarian cancer detection. Endocrine-Related Cancer 2004, 11: 63-178.
13. Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, Ward D, Williams K, Zhao H: Comparison of statistical methods for classifcation of ovarian cancer using mass spectrometry data. Bioinformatics 2003, 19:1636-1643.
14. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction New York: Springer-Verlag; 2001.
15. Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 2002, 99:6567-6572.
16. Supporting Documentation for Evaluating the Genetic Algorithm [http://krisa.ninds.nih.gov/gaweb/]
17. Baggerly K, Morris J, Coombes K: Reproducibility of SELDI-TOF protein patterns in serum comparing data sets from different experiments. Bioinformatics 2004, 20:777-785.
18. Efron B, Tibshirani R: An Introduction to the Bootstrap New York: Chapman & Hall; 1993.
19. Grizzle W, Meleth S: Clarification in the point/counterpoint discussion related to surface-enhanced laser desorption/ionization time-of-flight mass spectrometric identification of patients with adenocarcinomas of the prostate. Clinical Chemistry 2004, 50:1475-1476.
20. Petricoin E, Liotta L: Proteomic pattern complexity reveals a rich and uncharted continent of biomarkers. Clinical Chemistry 2004, 50:1476-1477.
21. Fung E, Enderwick C: ProteinChip Clinical proteomics: computational challenges and solutions. Biotechniques Compututational Proteomics Supplement 2002, 34:S34-S41.
22. The R Project for Statistical Computing [http://www.rproject.org/]
23. Biowulf Cluster at NIH [http://biowulf.nih.gov]