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Identifying
autoantibodies associated
with Alzheimer’s disease
DATA SCIENCE SUMMER SCHOOL 2016
PROJECT PRESENTATION
WE HAVE
90 samples
9353 proteins for each sample
How it looks
WE WANT
● to find most significantly differential proteins
between Alzheimer’s patients and healthy
controls
● to build a classifier
WHAT WE DO
Preprocess data:
WHAT WE DO
Preprocess data:
Take only significant proteins
WHAT WE DO
Preprocess data:
Take only significant proteins
Exclude highly correlated proteins
Take top 10 proteins (by p-value)
WHAT WE DO
Preprocess data:
Take only significant proteins
Exclude highly correlated proteins
Take top 10 proteins (by p-value)
PATIENTS
WHAT WE DO NEXT
Choose the model
Train the model
Get results
RAINFOREST
WHAT WE DO NEXT
Choose the model
Train the model
Get results
RAINFORESTRANDOM
98,7 % correctly predicted results in training set
92,6 % correctly predicted results in test set
p = 2.2e-16
98,7 % correctly predicted results in training set
92,6 % correctly predicted results in test set
p = 2.2e-16
WHAT DOES IT MEAN?
● PCBD2 + PTCD2 + CENTA2 + ANKHD1
● Support for previous research
● (Nagel et al., 2011)
● PCBD2 + PTCD2 as the heros
● (Han et al., 2012, Acharya et al., 2012)
WE
JUST DON'T ASK
Referencies:
Acharya, N.K., Nagele, E.P., Han, M., Coretti, N.J., DeMarshall, C., Kosciuk, M.C., Boulos, P.A. & Nagele,
R.G. (2012). Neuronal PAD4 expression and protein citrullination: possible role in production of
autoantibodies associated with neurodegenerative disease. Journal of Autoimmunity, 38(4):369-80.
Han, M., Nagele, E., DeMarshall, C., Acharya, N., &Nagele, R. (2012). Diagnosis of Parkinson's Disease
Based on Disease-Specific Autoantibody Profiles in Human Sera. PLoS ONE, 7(2): e32383.
Nagele, E., Han, M., DeMarshall, C., Belinka, B., & Nagele, R. (2011). Diagnosis of Alzheimer's Disease
Based on Disease-Specific Autoantibody Profiles in Human Sera. PLoS ONE, 6(8): e23112.

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Identifying autoantibodies associated with Alzheimer’s disease

  • 1. Identifying autoantibodies associated with Alzheimer’s disease DATA SCIENCE SUMMER SCHOOL 2016 PROJECT PRESENTATION
  • 2. WE HAVE 90 samples 9353 proteins for each sample
  • 3.
  • 5. WE WANT ● to find most significantly differential proteins between Alzheimer’s patients and healthy controls ● to build a classifier
  • 7. WHAT WE DO Preprocess data: Take only significant proteins
  • 8. WHAT WE DO Preprocess data: Take only significant proteins Exclude highly correlated proteins Take top 10 proteins (by p-value)
  • 9. WHAT WE DO Preprocess data: Take only significant proteins Exclude highly correlated proteins Take top 10 proteins (by p-value) PATIENTS
  • 10. WHAT WE DO NEXT Choose the model Train the model Get results RAINFOREST
  • 11. WHAT WE DO NEXT Choose the model Train the model Get results RAINFORESTRANDOM
  • 12. 98,7 % correctly predicted results in training set 92,6 % correctly predicted results in test set p = 2.2e-16
  • 13. 98,7 % correctly predicted results in training set 92,6 % correctly predicted results in test set p = 2.2e-16
  • 14. WHAT DOES IT MEAN? ● PCBD2 + PTCD2 + CENTA2 + ANKHD1 ● Support for previous research ● (Nagel et al., 2011) ● PCBD2 + PTCD2 as the heros ● (Han et al., 2012, Acharya et al., 2012)
  • 15.
  • 16. WE
  • 18. Referencies: Acharya, N.K., Nagele, E.P., Han, M., Coretti, N.J., DeMarshall, C., Kosciuk, M.C., Boulos, P.A. & Nagele, R.G. (2012). Neuronal PAD4 expression and protein citrullination: possible role in production of autoantibodies associated with neurodegenerative disease. Journal of Autoimmunity, 38(4):369-80. Han, M., Nagele, E., DeMarshall, C., Acharya, N., &Nagele, R. (2012). Diagnosis of Parkinson's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera. PLoS ONE, 7(2): e32383. Nagele, E., Han, M., DeMarshall, C., Belinka, B., & Nagele, R. (2011). Diagnosis of Alzheimer's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera. PLoS ONE, 6(8): e23112.