Analysis of premalignant pancreatic cancer mass spectrometry data for biomarker selection using a group search optimizer

Transactions of the Institute of Measurement and Control

Volumn 34, Issue 6, 2012, Pages 668-676

  Cui, Zhihua ^a   Shi, Zhongzhi ^a   Abonyi, Janos ^a   he, s ^a   Cooper, H J ^a   Ward, D ^a   Yao, x ^a   Heath, J K ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Cancer biomarkers; feature selection; group search optimization; proteomics

Indexed keywords

BIOMIMETICS; COMPUTER PROGRAMMING; DISCRIMINANT ANALYSIS; DISEASES; FEATURE EXTRACTION; GENETIC ALGORITHMS; MASS SPECTROMETRY; PARTICLE SWARM OPTIMIZATION (PSO); PROTEOMICS; SUPPORT VECTOR MACHINES;

CANCER BIOMARKERS; CLASSIFICATION PERFORMANCE; FEATURE SELECTION ALGORITHM; FEATURE SELECTION METHODS; GROUP SEARCH OPTIMIZER (GSO); LINEAR DISCRIMINANT ANALYSIS; OPTIMIZATION ALGORITHMS; SEARCH OPTIMIZATION;

BIOMARKERS;

EID: 84864221517     PISSN: 01423312     EISSN: 14770369     Source Type: Journal    
DOI: 10.1177/0142331211402893     Document Type: Article

References (27)

1. Alexandrov T, Decker J, Mertens B, Deelder AM, Tollenaar RAEM, Maass P, Thiele H (2009) Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation. Bioinformatics 25(5): 643–9.
2. Ambroise C, McLachlan G (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences USA 99: 6562–6.
3. Barnard CJ, Sibly RM (1981) Producers and scroungers: a general model and its application to captive flocks of house sparrows. Animal Behaviour 29: 543–50.
4. Bell JW (1990) Searching Behaviour – The Behavioural Ecology of Finding Resources. Chapman and Hall Animal Behaviour Series. London: Chapman and Hall.
5. Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International Group.
6. Carlson S, Najmi A, Whitin J, Cohen H (2005) Improving feature detection and analysis of surface-enhanced laser desorption/ionization-time of flight mass spectra. Proteomics 5: 2778–88.
7. Clerc M, Kennedy J (2002) The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation 6(1): 58–73.
8. Couzin I, Krause J, Franks N, Levin S (2005) Effective leadership and decision-making in animal groups on the move. Nature 434: 513–16.
9. Dixon AFG (1959) An experimental study of the searching behaviour of the predatory coccinellid beetle Adalia decempunctata. Journal of Animal Ecology 28: 259–81.
10. Dusenbery DB (1989) Ranging strategies. Journal of Theoretical Biology 136: 309–16.
11. Fogel DB (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. New York: IEEE Press.
12. Ge G, Wong GW (2008) Classification of premalignant pancreatic cancer massspectrometry data using decision tree ensembles. BMC Bioinformatics 9(1): 275.
13. Harper DGC (1988) Competitive foraging in mallards: ideal free ducks. Animal Behaviour 30: 575–84.
14. He S, Li X, Viant MR, Yao X (2009a) Profiling mass spectrometry proteomics data using smoothed nonlinear energy operator and Bayesian additive regression trees. Proteomics 9(17): 4176–91.
15. He S, Wu QH, Saunders JR (2009b) Group search optimizer – an optimization algorithm inspired by animal searching behaviour. IEEE Transactions on Evolutionary Computation 13: 973.
16. Mukhopadhyay S, Ray G (1998) A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Transactions on Biomedical Engineering 45: 180–7.
17. O’Brien WJ, Evans BI, Howick GL (1986) A new view of the predation cycle of a planktivorous fish, white crappie (Pomoxis annularis). Canadian Journal of Fisheries and Aquatic Sciences 43: 1894–9.
18. Pannala R, Basu A, Petersen GM, Chari ST (2009) New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer. The Lancet Oncology 10(1): 88–95.
19. Petricoin E, Ardekani A, Hitt B, Levine P, Fusaro V, Steinberg S, Mills G, Simone C, Fishman D, Kohn E, Liotta L (2002) Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359: 572–7.
20. Ressom H, Varghese R, Abdel-Hamid M, Abdel-Latif Eissa S, Saha D, Goldman L, Petricoin EF, Conrads TP, Veenstra TD, Loffredo CA, Goldman R (2005) Analysis of mass spectral serum profiles for biomarker selection. Bioinformatics 21(21): 4039–45.
21. Ressom H, Varghese R, Drake S, Hortin G, Abdel-Hamid M, Loffredo CA, Goldman R (2007) Peak selection from MALDI-TOF mass spectra using ant colony optimization. Bioinformatics 23: 619–626.
22. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19): 2507–17.
23. Schwefel HP (1995) Evolution and Optimum Seeking. New York: Wiley.
24. Vapnik V (1995) The Nature of Statistical Learning Theory. Berlin: Springer-Verlag.
25. Viswanathan GM, Buldyrev SV, Havlin S, da Luz MG, Raposo E, Stanley HE (1999) Optimizing the success of random searches. Nature 401: 911–14.
26. Yu J, Ongarello S, Fieldler R, Chen X, Toffolo G, Cobelli C, Trajanoski Z (2005) Ovarian cancer identification based on dimensionality reduction for highthroughput mass spectrometry data. Bioinformatics 21(10): 2200–9.
27. Zhang X, Wei D, Yap Y, Li L, Guo S, Chen F (2007) Mass spectrometry-based ‘omics’ technologies in cancer diagnostics. Mass Spectrometry Reviews 26: 403–31.