A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery

Bioinformatics

Volumn 25, Issue 17, 2009, Pages 2256-2262

  Yang, Zheng Rong ^a   Lertmemongkolchai, Ganjana ^b   Tan, Gladys ^c   Felgner, Philip L ^d   Titball, Richard ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b KHON KAEN UNIVERSITY   (Thailand)

^c DSO NATIONAL LABORATORIES   (Singapore)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL ANTIGEN; BIOLOGICAL MARKER;

ANTIGEN DETECTION; ARTICLE; BACTERIAL GENETICS; BACTERIUM CULTURE; BACTERIUM IDENTIFICATION; BLOOD ANALYSIS; BURKHOLDERIA PSEUDOMALLEI; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; GENETIC ANALYSIS; HUMAN; INFORMATION SCIENCE; JACKKNIFE TEST; MAJOR CLINICAL STUDY; MELIOIDOSIS; PREDICTION; PRIORITY JOURNAL; ROC CURVE;

ANTIGENS, BACTERIAL; BURKHOLDERIA INFECTIONS; BURKHOLDERIA PSEUDOMALLEI; COMPUTATIONAL BIOLOGY; EVOLUTION, MOLECULAR; HUMANS; MODELS, GENETIC; REPRODUCIBILITY OF RESULTS; ROC CURVE;

BURKHOLDERIA PSEUDOMALLEI;

EID: 69949187235     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp390     Document Type: Article

References (26)

1. Balestrieri,M.L. et al. (2008) Proteomics and cardiovascular disease: An update. Curr. Med. Chem., 15, 555-572.
2. Barbour,A.G. et al. (2008) A genome-wide proteome array reveals a limited set of immunogens in natural infections of humans and white-footed mice with Borrelia Burgdorferi. Infect Immun., 76 3374-3389.
3. Benhnia,M.R. et al. (2008) Redundancy and plasticity of neutralizing antibody responses are cornerstone attributes of the human immune response to the smallpox vaccine. J. Virol., 82, 3751-3768.
4. Carlsson,A. et al. (2008) Borrebaeck CA.Serum proteome profiling of metastatic breast cancer using recombinant antibody microarrays. Eur. J. Cancer, 44, 472-480.
5. Efron,B. (1981) Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika, 68, 589-599.
6. Eskin,E. and Siegel,E.V. (1999) Genetic programming applied to Othello: introducing students to machine learning research. In Joyce,D. ed. 30th Technical Symposium of the ACM Special Interest Group in Computer Science Education. Vol. 31.1, ACM Press, New Orleans, LA, USA, pp. 242-246.
7. Eyles,J.E. et al. (2007) Immunodominant Francisella tularensis antigens identified using proteome microarray. Proteomics, 7 2172.
8. Gerszten,R.E. and Wang,T.J. (2008) The search for new cardiovascular biomarkers. Nature, 451, 949-952.
9. Ge,G. and Wong,G.W. (2008) Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles. BMC Bioinformatics, 9, 275.
10. Goldberg,D.E. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading, MA.
11. Hamblin,C.L. (1957) Computer languages. Australian J. Sci., 20 135-139.
12. Han,K.Q. et al. (2008) Identification of lung cancer patients by serum protein profiling using surface-enhanced laser desorption/ ionization time-of-flight mass spectrometry. Am. J. Clin. Oncol., 31, 133-139.
13. Hanash,S.M. et al. (2008) Mining the plasma proteome for cancer biomarkers. Nature. 452, 571-579.
14. Hodgetts,A. et al. (2007) Biomarker discovery in infectious diseases using SELDI. Future Microbiol., 2, 35-49.
15. Ingvarsson,J. et al. (2008) Detection of pancreatic cancer using antibody microarray-based serum protein profiling. Proteomics, 8, 2211-2219.
16. Joachims,T. (1998) Text categorization with support vector machines: learning with many relevant features. In Nédellec,C. and Rouveirol,C. (eds) Proceedings of the European Conference on Machine Learning. Vol. 1398, Springer, London, UK, PP. 137-142.
17. Koza,J.R. (1992) Genetic Programming on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
18. Koza,J.R. and Rice,J.P. (1992) Automatic programming of robots using genetic programming. In Proceedings of Tenth National Conference on Artificial Intelligence. AAAI Press/MIT Press, Menlo Park, CA, USA, pp. 194-207.
19. Koza,J.R. and Andre,D. (1996a) Classifying protein segments as transmembrane domains using architecture-altering operations in genetic programming. In Angeline,P.J. and Kinnear,K.E. Jr. (eds). Advances in Genetic Programming II. MIT Press, Cambridge, MA, pp. 155-176.
20. Koza,J.R. and Andre,D. (1996b) Automatic discovery of protein motifs using genetic programming. In Yao,X. (ed). Evolutionary Computation: Theory and Applications. World Scientific, Singapore, PP. 171-197.
21. Loveard,T. and Ciesielski,V. (2001) Representing classification problems in genetic programming. Proc. Congress Evolut. Comput., 2, 1070-1077.
22. Lukaszewski,R.A. et al. (2008) The pre-symptomatic prediction of sepsis in intensive care unit patients: A pilot study. Clin Vaccine Immunol., 15, 1089-1094.
23. Metz,C.E. (1978) Basic principles of ROC analysis. Seminars in Nuclear Med., 8, 283-298.
24. Rexhepaj,E. et al. (2008) Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: Application to measurement of estrogen and progesterone receptor levels in breast cancer. Breast Cancer Res., 10, R89.
25. Vapnik,V. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, New York.
26. Williamson,Y.M. et al. (2008) Differentiation of Streptococcus pneumoniae conjunctivitis outbreak isolates by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ. Microbiol., 74, 5891-5897.