Deep learning methods hold strong promise for identifying biomarkers for
clinical application. However, current approaches for psychiatric
classification or prediction do not allow direct interpretation of
original features. In the present study, we introduce a sparse deep
neural network (DNN) approach to identify sparse and interpretable
features for schizophrenia (SZ) case–control classification. An L0-norm
regularization is implemented on the input layer of the network for
sparse feature selection, which can later be interpreted based on
importance weights.
Related Posts
Who Voted
Latest Posts
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
-
1
Upcoming Posts
-
2
-
2
-
2
-
2
-
2
-
2
-
2
-
2
-
2
-
2
Tag Cloud
Confidence Intervals for Random Forests
Abnormal involuntary movement scale in tardive dyskinesia
Plasma MnSOD Activity
mitochondria in the pathophysiology of schizophrenia
Brain Imaging
methamphetamine-associated psychosis
Oxidative Stress
Meta Analysis
Cognitive Function
Support Vector Machine
Neuroimaging in Python
schizophrenia
poisson regression
Advanced Machine Learning with Python
NUMPY
bipolar disorder
Immune response
Methamphetamine
Tardive dystonia
rbans
tardive dyskinesia
White blood cell count
cognitive impairment
smell identification test
[More]
Recent Visitors
Need an avatar? Get Gravatar!
Leave a comment