Characterization of pathogenic factors for premenstrual dysphoric disorder using machine learning algorithms in rats

Abstract

We established a methodology using machine learning algorithms for determining the pathogenic factors for premenstrual dysphoric disorder (PMDD). PMDD is a disease characterized by emotional and physical symptoms that occurs before menstruation in women of childbearing age. Owing to the diverse manifestations and various pathogenic factors associated with this disease, the diagnosis of PMDD is time-consuming and challenging. In the present study, we aimed to establish a methodology for diagnosing PMDD. Using an unsupervised machine-learning algorithm, we divided pseudopregnant rats into three clusters (C1 to C3), depending on the level of anxiety- and depression-like behaviors. From the results of RNA-seq and subsequent qPCR of the hippocampus in each cluster, we identified 17 key genes for building a PMDD diagnostic model using our original two-step feature selection with supervised machine learning. By inputting the expression levels of these 17 genes into the machine learning classifier, the PMDD symptoms of another group of rats were successfully classified as C1-C3 with an accuracy of 96%, corresponding to the classification by behavior. The present methodology would be applicable for the clinical diagnosis of PMDD using blood samples instead of samples from the hippocampus in the future.

Keywords: Clustering; Hippocampus; Machine learning; PMDD.