Ensemble learning for higher diagnostic precision in schizophrenia using peripheral blood gene expression profile

The need for molecular biomarkers for schizophrenia has been well recognized. Peripheral blood gene expression profiling and machine learning (ML) tools have recently become popular for biomarker discovery. The stigmatization associated with schizophrenia advocates the need for diagnostic models with higher precision. In this study, we propose a strategy to develop higher-precision ML models using ensemble learning. We performed a meta-analysis using peripheral blood expression microarray data. The ML models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes as features. The ensemble of SVM-radial and PAM predicted test samples with a precision of 81.33% (SD: 0.078). The precision of the ensemble model was significantly higher than SVM-radial (63.83%, SD: 0.081) and PAM (66.89%, SD: 0.097). The feature genes identified were enriched for biological processes such as response to stress, response to stimulus, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis of feature genes identified PRF1, GZMB, IL2RB, ITGAL , and IL2RG as hub genes. Additionally, the ensemble model developed using microarray data classified the RNA-Sequencing samples with moderately high precision (72.00%, SD: 0.08). The pipeline developed in this study allows the prediction of a single microarray and RNA-Sequencing sample. In summary, this study developed robust models for clinical application and suggested ensemble learning for higher diagnostic precision in psychiatric disorders. Research highlights ![Figure][1] Graphical abstract Blood based SCZ diagnosis using ensemble learning for higher precision ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The study was funded by an intramural research grant (MjRP/19-20/1516) from Symbiosis Centre for Research & Innovation (SCRI), SIU, Pune, India. The first author of this study received research fellowships from UGC, New Delhi, to carry out this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: KEM Hospital Research Centre Ethics Committee (KEMHRC ID No. 2001) and Symbiosis International (Deemed University) Independent Ethics Committee (SIU/IEC/99) approved the study protocol. Informed consent was obtained from all the participants. The consent for participants with schizophrenia was supported by the consent of a first-degree relative. Clinical interviews were administered by a trained psychiatrist and a psychologist. The diagnosis was confirmed by a senior psychiatrist. All the participants were compensated for their travel and time. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data availability: The RNA-Sequencing data of this study will be available from the corresponding authors upon publication. Code availability: The R scripts used for the analysis are available on GitHub ([https://github.com/macdlab/2023\_VW\_SCZ_Ensemble][2]) [https://github.com/macdlab/2023\_VW\_SCZ_Ensemble][2] [1]: pending:yes [2]: https://github.com/macdlab/2023_VW_SCZ_Ensemble