DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach

Suicide is one of the leading causes of death. Major Depressive Disorder (MDD) is one of the risk factors for committing suicide. Epigenetic data may help to distinguish suicidal and non-suicidal deaths. The objective of this study is to use machine learning to predict suicidal and non-suicidal deaths from DNA methylation data. We used support-vector machines (SVMs) to classify existing secondary data consisting of normalized values of methylated DNA probe intensities from tissues of two cortical brain regions (Brodmann Area 11 [BA11] and Brodmann Area 25 [BA25]) to distinguish 20 suicide cases (who died following a major depressive disorder) from 20 control cases (non-psychiatric sudden death). Prior to classification, we employed Principal Components Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimension of the data. In comparison to PCA, the modern data visualization method t-SNE performs better in dimensionality reduction. We applied four-fold cross-validation in which the resulting output from t-SNE was used as training data for the SVM. The Receiver Operating Characteristic Curve (ROC) for the classification exhibited an 80% Area Under the Curve (AUC) for BA25 data and 100% AUC for BA11. This research constitutes a baseline study for classifying suicidal and non-suicidal deaths from DNA methylation data. Future studies with larger sample size may reduce the bias and improve the accuracy of the results.