ABCD challenge outcome

Leo Brueggeman

Acknowledgements - "modelers"

Tanner Koomar

Brady Hoskins

Yongchao Huang

Tien Tong

James Kent

Approach

  • Kaggle inspired

Approach

  • Kaggle inspired

Approach

  • Kaggle inspired

Data

N = ~ 3700

>400 volumes

test

N>4000

(unlabeled)

y

age

sex

collection site

SES

total brain volume

Data

N = ~ 3700

>400 volumes

Data

Data

Approach

N = ~ 3700

>400 volumes

N = 3000

N = 700

train

validation

models

ensemble model

test

N>4000

(unlabeled)

Approach

train

R

+

Approach

train

Hyperparameter optimization in CV

     - error metric: MSE

 

Linear models

 

Decision Trees

 

​Boosting

Approach

train

Approach

validation

Approach

validation

Conclusion

Intelligence prediction from ensemble model in CV: Pearson's R = 0.12

 

Machine learning competitions are a great opportunity for building skills in a new scientific area (Encode)

 

Teamwork lessons:

     github > in person meetings

     starter code

 

Going forward:

     modeling ABCD phenotypes with brain volumes (collaborators)

 

 

ABCD challenge outcome

By leoo

ABCD challenge outcome

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