Original Article

Citation: Translational Psychiatry (2016) 6, e931; doi:10.1038/tp.2016.198
Published online 25 October 2016

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder

J S Yu1, A Y Xue1, E E Redei2 and N Bagheri1

  1. 1Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA
  2. 2Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA

Correspondence: Professor N Bagheri, Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, 2145 Sheridan Road, E154, Evanston, IL 60208, USA. E-mail: n-bagheri@northwestern.edu

Received 13 June 2016; Revised 3 August 2016; Accepted 17 August 2016



Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure—including equipment, trained personnel, billing, and governmental approval—for similar tests is well established in clinics worldwide. Here we present a supervised classification model utilizing support vector machines (SVMs) for the analysis of transcriptomic data readily obtained from a peripheral blood specimen. The model was trained on data from subjects with MDD (n=32) and age- and gender-matched controls (n=32). This SVM model provides a cross-validated sensitivity and specificity of 90.6% for the diagnosis of MDD using a panel of 10 transcripts. We applied a logistic equation on the SVM model and quantified a likelihood of depression score. This score gives the probability of a MDD diagnosis and allows the tuning of specificity and sensitivity for individual patients to bring personalized medicine closer in psychiatry.