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Schizophrenia Research
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Volume 214, Pages 1-76 (December 2019)
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1. Editorial Board
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2. Guest editorial: Special issue on machine learning in schizophrenia
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M. Mallar Chakravarty
3. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study
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Julie L. Winterburn, Aristotle N. Voineskos, Gabriel A. Devenyi, Eric Plitman, M. Mallar Chakravarty
4. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI
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Yuan Xiao, Zhihan Yan, Youjin Zhao, Bo Tao, Su Lui
5. Individualized prediction of psychosis in subjects with an at-risk mental state
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Eleni Zarogianni, Amos J. Storkey, Stefan Borgwardt, Renata Smieskova, Stephen M. Lawrie
6. Effective connectivity within a triple network brain system discriminates schizophrenia spectrum disorders from psychotic bipolar disorder at the single-subject level
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Lena Palaniyappan, Gopikrishna Deshpande, Pradyumna Lanka, D. Rangaprakash, Peter F. Liddle
7. Improving individual predictions: Machine learning approaches for detecting and attacking heterogeneity in schizophrenia (and other psychiatric diseases)
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Hugo G. Schnack
8. Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods
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Nicolas Honnorat, Aoyan Dong, Eva Meisenzahl-Lechner, Nikolaos Koutsouleris, Christos Davatzikos
9. Identifying schizophrenia subgroups using clustering and supervised learning
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Alexandra Talpalaru, Nikhil Bhagwat, Gabriel A. Devenyi, Martin Lepage, M. Mallar Chakravarty
10. Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP)
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Suraj Sarvode Mothi, Mukund Sudarshan, Neeraj Tandon, Carol Tamminga, Matcheri S. Keshavan
11. Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype
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Neeraj Tandon, Rajiv Tandon
