MS-ACGAN: A modified auxiliary classifier generative adversarial network for schizophrenia's samples augmentation based on microarray gene expression data

Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although machine learning models have been widely applied in medical data analysis, some barriers are yet to be challenging, such as available biosample shortage, prohibitive costs, rare diseases, and ethical considerations. Transcriptomics, an omics approach that studies gene activities and provides gene expression data such as microarray and RNA-Sequences faces the difficulties of biospecimen collection, particularly for mental disorders, as some psychiatric patients avoid medical care. Microarray data suffers from the low number of available samples, making it challenging to apply machine learning models. However, adversarial generative network (GAN), the hottest paradigm in deep learning, has created unprecedented momentum in data augmentation and efficiently expands datasets. This paper proposes a novel model termed MS-ACGAN, where the generator feeds on a bordered Gaussian distribution. In machine learning, calibration is of utmost importance, which gives insight into model uncertainty and is considered a crucial step toward improving the robustness and reliability of models. Therefore, we apply calibration techniques to classifiers and focus on estimating their probabilities as accurately as possible. Additionally, we present our trustworthy outputs by harnessing confidence intervals that confine the point estimate limitations and report a range of expected values for performance metrics. Both concepts statistically describe the implemented model's reliability in this study. Furthermore, we employ two quantitative measures, GAN-train and GAN-test, to demonstrate that the artificial data generated by our robust approach remarkably resembles the original data characteristics. [Display omitted] •Developing a novel GAN architecture by re-engineering the generator for schizophrenia microarray sample augmentation.•Enriching GAN evaluation measures using confidence interval and calibration.•Generating artificial samples significantly close to the original samples.