A Blood-Based Transcriptomic Signature for PTSD Classification Using Machine Learning
Alipour, S.; Pamanji, R.; Jamil, E.; Yeguvapalli, S.; Chitrala, K. N.
Show abstract
Post-traumatic stress disorder (PTSD) remains a significant psychiatric burden; despite growing biomarker research, no blood-based molecular diagnostic tool has been clinically validated for routine use. In this study, we developed a machine learning classifier for PTSD using peripheral blood leukocyte RNA-seq data from combat-exposed U.S. Marines (GSE64813), diagnosed via the Clinician-Administered PTSD Scale (CAPS) under DSM-IV criteria. Differentially expressed genes (DEGs) were identified and further refined through additional filtering criteria, yielding a 90-gene feature set used to train and compare multiple machine learning models. The support vector machine (SVM) classifier achieved the best performance, with an accuracy of 89% and an AUC of 0.95, outperforming logistic regression and random forest approaches. Furthermore we evaluated our model on independent external datasets to assess generalizability. These findings highlight the promise of transcriptomic signatures as a foundation for objective, blood-based PTSD diagnostics, while emphasizing the critical need for robust cross-dataset generalizability. Code availabilityhttps://www.kaggle.com/code/persianexxx/ptsd-final
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