Parkinson's disease prediction using gene expression- A projection based learning meta-cognitive neural classifier approach

Expert Systems with Applications

Volumn 40, Issue 5, 2013, Pages 1519-1529

  Sateesh Babu, G ^a   Suresh, S ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Dysphonia; Gait analysis; Meta cognitive learning; Microarray gene expression; Parkinson disease; Radial basis function network

Indexed keywords

ANOVA TEST; COGNITIVE COMPONENTS; COMPUTATIONAL EFFORT; DATA SETS; DYSPHONIA; GENE EXPRESSION DATA; HUMAN LEARNING; LEARNING PROCESS; LEARNING STRATEGY; METACOGNITIVES; MICROARRAY GENE EXPRESSION; NEURAL CLASSIFIERS; PARKINSON DISEASE; PARKINSON'S DISEASE; TWO-COMPONENT;

CLASSIFICATION (OF INFORMATION); FORECASTING; GAIT ANALYSIS; GENE EXPRESSION; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84872035386     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.08.070     Document Type: Article

References (48)

1. M. Banaie, M. Pooyan, and M. Mikaili Introduction and application of an automatic gait recognition method to diagnose movement disorders that arose of similar causes Expert Systems with Applications 38 2011 7359 7363
2. M.F. Caglar, B. Cetisli, and I.B. Toprak Automatic recognition of Parkinson's disease from sustained phonation tests using ANN and adaptive neuro-fuzzy classifier Journal of Engineering Science and Design 1 2010 59 64
3. C.-C. Chang, and C.-J. Lin LIBSVM: A library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 2011 27:1 27:27 http://www.csie.ntu.edu.tw/cjlin/libsvm
4. Y.-Y. Chen, C.-W. Cho, S.-H. Lin, H.-Y. Lai, Y.-C. Lo, and S.-Y. Chen A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences Expert Systems with Applications 39 2012 520 526
5. C.-W. Cho, W.-H. Chao, S.-H. Lin, and Y.-Y. Chen A vision-based analysis system for gait recognition in patients with Parkinson's disease Expert Systems with Applications 36 2009 7033 7039
6. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 1995 273 297
7. R. Das A comparison of multiple classification methods for diagnosis of Parkinson disease Expert Systems with Applications 37 2010 1568 1572
8. M. Engin, S. Demirag, E.Z. Engin, G. Celebi, F. Ersan, and E. Asena The classification of human tremor signals using artificial neural network Expert Systems with Applications 33 2007 754 761 (Pubitemid 46366679)
9. O. Eskidere, F. Ertas, and C. Hanilci A comparison of regression methods for remote tracking of Parkinson's disease progression Expert Systems with Applications 39 2012 5523 5528
10. Gavert, H., Hurri, J., Sarela, J., & Hyvarinen, A. (2005). The FastICA package for MATLAB. .
11. P.-F. Guo, P. Bhattacharya, and N. Kharma Advances in detecting Parkinson's disease D. Zhang, M. Sonka, Medical biometrics Lecture notes in computer science vol. 6165 2010 Springer Berlin/ Heidelberg 306 314
12. J.M. Hausdorff, J. Lowenthal, T. Herman, L. Gruendlinger, C. Peretz, and N. Giladi Rhythmic auditory stimulation modulates gait variability in Parkinson's disease European Journal of Neuroscience 26 2007 2369 2375 (Pubitemid 47620477)
13. A. Hyvarinen Fast and robust fixed-point algorithms for independent component analysis IEEE Transactions on Neural Networks 10 1999 626 634
14. R. Isaacson, and F. Fujita Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning Journal of the Scholarship of Teaching and Learning 6 2006 39 55
15. N. Japkowicz, and M. Shah Evaluating learning algorithms: a classification perspective 2011 Cambridge University Press
16. Jeon, H.-S., Han, J., Yi, W.-J., Jeon, B., & Park, K. S. (2008). Classification of Parkinson gait and normal gait using spatial-temporal image of plantar pressure. In: Engineering in medicine and biology society. 30th Annual international conference of the IEEE (pp. 4672-4675).
17. S.-H. Lee, and J.S. Lim Parkinson's disease classification using gait characteristics and wavelet-based feature extraction Expert Systems with Applications 39 2012 7338 7344
18. M. Little, P. McSharry, E. Hunter, J. Spielman, and L. Ramig Suitability of dysphonia measurements for telemonitoring of Parkinson's disease IEEE Transactions on Biomedical Engineering 56 2009 1015 1022
19. M. Little, P. McSharry, S. Roberts, D.A.E. Costello, and I. Moroz Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection BioMedical Engineering Online 6 2007
20. A. Manciocco, F. Chiarotti, A. Vitale, G. Calamandrei, G. Laviola, and E. Alleva The application of Russell and Burch 3R principle in rodent models of neurodegenerative disease: The case of Parkinson's disease Neuroscience and Biobehavioral Reviews 33 2009 18 32
21. S. Pan, S. Iplikci, K. Warwick, and T.Z. Aziz Parkinson's Disease tremor classification-A comparison between support vector machines and neural networks Expert Systems with Applications 39 2012 10764 10771
22. K. Polat Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering International Journal of Systems Science 43 2012 597 609
23. I. Psorakis, T. Damoulas, and M. Girolami Multiclass relevance vector machines: Sparsity and accuracy IEEE Transactions on Neural Networks 21 2010 1588 1598
24. W.P. Rivers Autonomy at all costs: An ethnography of metacognitive self-Assessment and self-management among experienced language learners The Modern Language Journal 85 2001 279 290 (Pubitemid 33778031)
25. C. Rodriguez-Solano, and L.M. Laita An algebraic approach to Parkinson disease diagnosis Expert Systems with Applications 37 2010 7997 8005
26. C. Sakar, and O. Kursun Telediagnosis of Parkinson's disease using measurements of dysphonia Journal of Medical Systems 34 2010 591 599
27. Sateesh Babu, G., Savitha, R., & Suresh, S. (2012a). A projection based learning in meta-cognitive radial basis function network for classification problems. In: The 2012 international joint conference on neural networks (IJCNN) (pp. 2907-2914).
28. G. Sateesh Babu, and S. Suresh Meta-cognitive neural network for classification problems in a sequential learning framework Neurocomputing 81 2012 86 96
29. G. Sateesh Babu, S. Suresh, K. Uma Sangumathi, and H. Kim A projection based learning meta-cognitive rbf network classifier for effective diagnosis of Parkinson's disease J. Wang, G. Yen, M. Polycarpou, Advances in Neural Networks- ISNN 2012 Lecture Notes in Computer Science Vol. 7368 2012 611 620
30. R. Savitha, S. Suresh, and N. Sundararajan A meta-cognitive learning algorithm for a fully complex-valued relaxation network Neural Networks 32 2012 209 218
31. R. Savitha, S. Suresh, and N. Sundararajan Metacognitive learning in a fully complex-valued radial basis function neural network Neural Computation 24 2012 1297 1328
32. C.R. Scherzer, A.C. Eklund, L.J. Morse, Z. Liao, J.J. Locascio, and D. Fefer Molecular markers of early Parkinson's disease based on gene expression in blood Proceedings of the National Academy of Sciences 104 2007 955 960 (Pubitemid 46154718)
33. B. Shahbaba, and R. Neal Nonlinear models using dirichlet process mixtures Journal of Machine Learning Research 10 2009 1829 1850
34. G.K. Smyth Linear models and empirical Bayes methods for assessing differential expression in microarray experiments Statistical Applications in Genetics and Molecular Biology 3 2004 Article 3
35. F. Strom, and R. Koker A parallel neural network approach to prediction of Parkinson's disease Expert Systems with Applications 38 2011 12470 12474
36. K. Subramanian, and S. Suresh A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system Applied Soft Computing 12 2012 3603 3614
37. S. Suresh, R.V. Babu, and H. Kim No-reference image quality assessment using modified extreme learning machine classifier Applied Soft Computing 9 2009 541 552
38. S. Suresh, K. Dong, and H.J. Kim A sequential learning algorithm for self-Adaptive resource allocation network classifier Neurocomputing 73 2010 3012 3019
39. S. Suresh, S.N. Omkar, V. Mani, and T.N.G. Prakash Lift coefficient prediction at high angle of attack using recurrent neural network Aerospace Science and Technology 7 2003 595 602
40. S. Suresh, R. Savitha, and N. Sundararajan A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN IEEE Transactions on Neural Networks 22 2011 1061 1072
41. S. Suresh, N. Sundararajan, and P. Saratchandran Risk-sensitive loss functions for sparse multi-category classification problems Information Sciences 178 2008 2621 2638
42. S. Suresh, N. Sundararajan, and P. Saratchandran A sequential multi-category classifier using radial basis function networks Neurocomputing 71 2008 1345 1358 (Pubitemid 351373197)
43. Taccioli, C., Maselli, V., Tegner, J., Gomez-Cabrero, D., Altobelli, G., Emmett, W., Lescai, F., Gustincich, S., & Stupka, E. (2011). ParkDB: A Parkinson's disease gene expression database. .abstract.
44. N.M. Tahir, and H.H. Manap Parkinson disease gait classification based on machine learning approach Journal of Applied Sciences 12 2012 180 185
45. A.L. Wenden Metacognitive knowledge and language learning Applied Linguistics 19 1998 515 537 (Pubitemid 128382080)
46. D. Wu, K. Warwick, Z. Ma, J.G. Burgess, S. Pan, and T.Z. Aziz Prediction of Parkinson's disease tremor onset using radial basis function neural networks Expert Systems with Applications 37 2010 2923 2928
47. T. Zhang Statistical behavior and consistency of classification methods based on convex risk minimization Annals of Statistics 32 2004 56 85
48. H. Zheng, M. Yang, H. Wang, and S. McClean Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis S. McClean, P. Millard, E. El-Darzi, C. Nugent, Intelligent patient management Studies in computational intelligence vol. 189 2009 Springer Berlin/ Heidelberg 57 70