A5051781290- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Genetic Associations and Epidemiology
Introduction: Stigma contributes to a significant part of the burden schizophrenia (SCZ), therefore reducing false positives from diagnosis would be liberating for individuals with SCZ and desirable clinicians. The stigmatization associated advocates need high-precision diagnosis. In this study, we present an ensemble learning-based approach using peripheral blood gene expression profiles. Methodology: machine learning (ML) models, support vector machines (SVM), prediction analysis...
Abstract The need for molecular biomarkers schizophrenia has been well recognized. Peripheral blood gene expression profiling and machine learning (ML) tools have recently become popular biomarker discovery. stigmatization associated with advocates the diagnostic models higher precision. In this study, we propose a strategy to develop higher-precision ML using ensemble learning. We performed meta-analysis peripheral microarray data. models, support vector machines (SVM), prediction analysis...