Biomarker based classification and clustering of Veterans with PTSD

创伤后应激障碍(PTSD)对社会和 以个人为基础。我们需要为退伍军人提供更好的治疗，并在方法上 进展可以促进对创伤后应激障碍的理解和治疗的进步。 随着DNA收集和分析成本的显著降低， 现在有许多包含相对全面的基因数据的大型储存库。 此外，在个人层面上，许多退伍军人拥有可用的遗传数据 通过商业广告(如23和我)或研究(如百万老兵计划) 实体。访问他们自己的基因数据可能会让人们利用 在接受最先进的个性化目标方面的最新研究进展 医药。新的算法进步可以使这些数据被用于改进 我们对创伤后应激障碍等病症的理解。机器学习提供了一种方法 通过增强树或深度学习等方法更好地识别患有创伤后应激障碍的人 模特们。聚类技术可以提供一种澄清同质子群的方法 在老兵的样本中。 该项目希望采取监督(又名，分类)和非监督 (也称为聚类)分析方法，以更好地了解创伤后应激障碍，使用遗传和 来自公开可用的数据仓库的神经成像数据。对于这部作品，其特点是 根据文献中的最新进展和模型进行选择 通过严格的实证评估。治疗创伤后应激障碍和其他疾病的方法是 可能受到基于症状报告的分类的限制，例如在 需求侧管理。这种传统的方法创造了生物上不同种类的样本。我们 建议将先进的分析工具应用于经验性衍生的生物标志物，以创建 基于生物的同质分组。

Posttraumatic Stress Disorder (PTSD) is both impactful on a societal and individual basis. Better treatments are needed for our Veterans, and methodological advancements can facilitate progress in the understanding and treatment of PTSD. With the dramatically reduced cost of collection and analysis of DNA there is now many large repositories that contain relatively comprehensive genetic data. Furthermore, on the individual level, many Veterans have genetic data available through commercial (e.g., 23 and Me) or research (e.g., the Million Veteran Program) entities. It is possible that access to their own genetic data will allow people to leverage the latest research developments in the aims of receiving state-of-the-art personalized medicine. New algorithmic advances can allow this data to be utilized for improving our understanding of conditions, such as PTSD. Machine learning provides a way to better identify those with PTSD through methods such as boosted trees or deep learning models. Clustering techniques can provide a way to clarify homogenous subgroups within Veteran samples. This project looks to take a supervised (aka, classification) and unsupervised (aka, clustering) analytical approach to better understand PTSD using genetic and neuroimaging data from publicly available data repositories. For this work, features are selected based on the latest advancements in the literature and models are selected through rigorous empirical evaluation. Treatment for PTSD, and other disorders, is potentially limited by classifications that are based on symptom reports such as used in DSM. This traditional approach creates biologically heterogeneous samples. We propose applying advanced analytical tools to empirically derived biomarkers to create homogeneous biologically-based groupings.