http://iet.metastore.ingenta.com
1887

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.

References

    1. 1)
      • (2012)
        1. Parkinson's UK: ‘Parkinson's prevalence in the United Kingdom. 2009’ (Parkinson's UK, London, 2012), pp. 113.
        .
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • A. Rajput , B. Rozdilsky , A. Rajput .
        6. Rajput, A., Rozdilsky, B., Rajput, A.: ‘Accuracy of clinical diagnosis in Parkinsonism – a prospective study’, Can. J. Neurol. Sci.. Le J. Can. des Sci. Neurolog., 1991, 18, (3), pp. 275278.
        . Can. J. Neurol. Sci.. Le J. Can. des Sci. Neurolog. , 3 , 275 - 278
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • K.A. Wesnes , D.J. Burn .
        20. Wesnes, K.A., Burn, D.J.: ‘The prevalence and nature of mild cognitive impairment in Parkinson's disease (PD-MCI) identified using automated cognitive tests[abstract]. Mov. Disord., 2013, 28,(Suppl 1), p. 568.
        . Mov. Disord. , 568
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • M.C. Barone , D. Bohmann .
        26. Barone, M.C., Bohmann, D.: ‘Assessing neurodegenerative phenotypes in drosophila dopaminergic neurons by climbing assays and whole brain immunostaining’, J Visualized Experiments: JoVE, 2013, (74).
        . J Visualized Experiments: JoVE , 74
    27. 27)
    28. 28)
      • M. Phillips , S. Roberts , N. Kladt .
        28. Phillips, M., Roberts, S., Kladt, N., et al: ‘An automated, high-throughput climbing assay for behavioral screening in drosophila’, Front. Behav. Neurosci., p. 357.
        . Front. Behav. Neurosci. , 357
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
      • A.H. Brand , N. Perrimon .
        37. Brand, A.H., Perrimon, N.: ‘Targeted gene expression as a means of altering cell fates and generating dominant phenotypes’, Development, 1993, 118, (2), pp. 401415.
        . Development , 2 , 401 - 415
    38. 38)
    39. 39)
    40. 40)
    41. 41)
      • M. Granato , F. Van Eeden , U. Schach .
        41. Granato, M., Van Eeden, F., Schach, U., et al: ‘Genes controlling and mediating locomotion behavior of the zebrafish embryo and larva’, Development, 1996, 123, (1), pp. 399413.
        . Development , 1 , 399 - 413
    42. 42)
    43. 43)
    44. 44)
      • Y. Lei , X. Guo , Y. Liu .
        44. Lei, Y., Guo, X., Liu, Y., et al: ‘Efficient targeted gene disruption in xenopus embryos using engineered transcription activator-like effector nucleases (Talens)’. Proc. of the National Academy of Sciences, 2012, p. 201215421.
        . Proc. of the National Academy of Sciences , 201215421
    45. 45)
    46. 46)
    47. 47)
      • R. Chiong , T. Weise , Z. Michalewicz . (2012)
        47. Chiong, R., Weise, T., Michalewicz, Z.: ‘Variants of evolutionary algorithms for real-world applications’ (Springer, 2012).
        .
    48. 48)
      • J.F. Miller . (2011)
        48. Miller, J.F.: ‘Cartesian genetic programming’ (Springer, 2011).
        .
    49. 49)
      • M. Lones , S.L. Smith , A.T. Harris .
        49. Lones, M., Smith, S.L., Harris, A.T., et al: ‘Discriminating normal and cancerous thyroid cell lines using implicit context representation cartesian genetic programming’. 2010 IEEE Congress on Evolutionary Computation (CEC), 2010.
        . 2010 IEEE Congress on Evolutionary Computation (CEC)
    50. 50)
      • M.A. Lones , J.E. Alty , P. Duggan-Carter .
        50. Lones, M.A., Alty, J.E., Duggan-Carter, P., et al: ‘Classification and characterisation of movement patterns during levodopa therapy for Parkinson's disease’. Proc. of the 2014 Conf. Companion on Genetic and Evolutionary Computation Companion, 2014.
        . Proc. of the 2014 Conf. Companion on Genetic and Evolutionary Computation Companion
    51. 51)
    52. 52)
    53. 53)
    54. 54)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2015.0030
Loading

Related content

content/journals/10.1049/iet-syb.2015.0030
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address