| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study
|
Winterburn, Julie L.
|
|
|
214 |
C |
p. 3-10
|
article
|
| 2 |
Editorial Board
|
|
|
|
214 |
C |
p. IFC
|
article
|
| 3 |
Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level
|
Palaniyappan, Lena
|
|
|
214 |
C |
p. 24-33
|
article
|
| 4 |
Guest editorial: Special issue on machine learning in schizophrenia
|
Chakravarty, M. Mallar
|
|
|
214 |
C |
p. 1-2
|
article
|
| 5 |
Identifying schizophrenia subgroups using clustering and supervised learning
|
Talpalaru, Alexandra
|
|
|
214 |
C |
p. 51-59
|
article
|
| 6 |
Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)
|
Schnack, Hugo G.
|
|
|
214 |
C |
p. 34-42
|
article
|
| 7 |
Individualized prediction of psychosis in subjects with an at-risk mental state
|
Zarogianni, Eleni
|
|
|
214 |
C |
p. 18-23
|
article
|
| 8 |
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP)
|
Mothi, Suraj Sarvode
|
|
|
214 |
C |
p. 60-69
|
article
|
| 9 |
Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods
|
Honnorat, Nicolas
|
|
|
214 |
C |
p. 43-50
|
article
|
| 10 |
Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI
|
Xiao, Yuan
|
|
|
214 |
C |
p. 11-17
|
article
|
| 11 |
Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype
|
Tandon, Neeraj
|
|
|
214 |
C |
p. 70-75
|
article
|
Journal description