Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.

Abstract

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavior. Physical detection of MDD patients is less precise but machine learning can aid in improved classification of disease. The present research included three RNA-seq data classes to classify DEGs and then train key gene data using a randomforest machine learning method. The three classes in the sample are 29 CON (sudden death healthy control), 21 MDD-S (a Major DepressiveDisorder Suicide) being included in the second group, and 9 MDD (non-suicides MDD) which are included in the third group. With PCA analysis, 99 key genes were obtained. 47.1% data variability is given by these 99 genes. The model training of 99 genes indicated improved classification. The RF classification model has an accuracy of 61.11% over test data and 97.56% over train data. It was also noticed that the RF method offered greater accuracy than the KNN method. 99 genes were annotated using DAVID and ClueGo packages. Some of the important pathways and function observed in the study were glutamatergic synapse, GABA receptor activation, long-term synaptic depression, and morphine addiction. Out Of 99 genes, four genes, namely DLGAP1, GNG2, GRIA1, and GRIA4, were found to be predominantly involved in the glutamatergic synapse pathway. Another substantial link was observed in the GABA receptor activation involving the following two genes, GABBR2 and GNG2. Also, the genes found responsible for long-term synaptic depressionwere GRIA1, MAPT, and PTEN. There was another finding of morphine addiction which comprises three genes, namely GABBR2, GNG2, and PDE4D. For massive datasets, this approach will act as the gold standard. The cases of CON, MDD, and MDD-S are physically distinct. There was dysregulation in the expression level of 12 genes. The 12 genes act as a possible biomarker for Major Depressive Disorder and open up a new path for depressed subjects to explore further.