title: A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder.
abstract: 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.
authors: Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
authors: J S
authors: JS
authors: Yu
authors: Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
authors: A Y
authors: AY
authors: Xue
authors: Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA.
authors: E E
authors: EE
authors: Redei
authors: Chemical and Biological Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
authors: N
authors: N
authors: Bagheri
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D003865:Major Depressive Disorder / Q000175:diagnosis* / Q000235:genetics* / Q000523:psychology; D005260:Female; D020869:Gene Expression Profiling; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008960:Models, Psychological; D057285:Precision Medicine; D011237:Predictive Value of Tests; D011336:Probability; D060388:Support Vector Machine
chemical_list: D005819:Genetic Markers