Corrigendum to “A Characteristic Cerebellar Biosignature for Bipolar Disorder, Identified with Fully Automatic Machine Learning” [IBRO Neurosci Rep. 15 (2023) 77–89]

The authors regret the affiliation of our last co-author Ioannis Tsamardinos was incorrectly listed as "Greek National Health System, G. Papanikolaou General Hospital, Organizational Unit - Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece", instead the correct affiliation is: "Department of Computer Science, University of Crete, Heraklion, Greece". Also, the title (Prof), should have been removed from the affiliations, for reasons of simplicity. The authors would like to apologise for any inconvenience caused. A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learningIBRO Neuroscience ReportsVol. 15PreviewTranscriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Full-Text PDF Open Access