A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning

Abstract

AbstractBackroundTranscriptomic 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.MethodA fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array.ResultsPatients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. Using 6 of the characteristic features (genes) discovered during the selection process, 99,6% of predictive performance was achieved. The 3 genes contributing most to the predictive power of the model (92,7% predictive performance) are also deregulated in temporal lobe epilepsy. KEGG analysis revealed participation of 4 identified features in 6 pathways which have been associated with bipolar disorder.Conclusion93% Area Under Curve, 96% Average Precision, and complete separation between unaffected controls and patients with bipolar disorder, were achieved in ∼2 hours. The cerebellar transcriptomic biosignature suggests a potential genetic overlap with temporal lobe epilepsy and new genetic contributions to the pathogenesis of bipolar disorder.