Integrative Predictors of Temporomandibular Osteoarthritis

摘要 该应用程序建议开发高效的基于Web的数据管理、挖掘和分析， 整合并分析来自TMJ OA患者临床、生物学和高维度成像数据。基于 我们发表的结果，我们假设髁突骨结构，临床症状， 生物介质是TMJOA进展严重性的未被认识的指标。有效 以最大化其价值的方式捕获、策划、管理、集成和分析这些数据， 可及性对于科学进步和这种综合性TMJ OA患者的益处至关重要， 信息可以使。高维数据库越来越难以处理使用手头 数据库管理工具或传统的处理应用程序，创造了创新的持续需求， 接近。为此，密歇根大学的DCBIA与北方大学合作， 卡罗莱纳，德克萨斯大学医学博士安德森癌症中心和Kitware公司。通过高维 在分子、临床和影像学水平上，我们将确定TMJ OA患者的定量特征， 有更严重预后风险的表型，以及未来治疗的靶点。 拟议的基于网络的系统，数据存储计算和集成（DSCI），将远程 计算机器学习、图像分析和高级统计数据， TMJ OA患者的数据。由于其在网络中无处不在的设计，DSCI软件的安装将不再 被要求。我们的长期目标是创建软件和数据库的颞下颌关节骨关节炎。等 存储库需要在分布式计算环境中维护数据，以允许对 数据库从多个临床中心和共享训练模型TMJ分类。在第四年和第五年， 建议的工作，在北方大学牙科学院的临床医生的传播和培训 明尼苏达大学和俄勒冈州健康科学学院的卡罗尔将允许扩大拟议的研究。在目标1中， 我们将测试最先进的神经网络结构，以开发一个组合软件模块，其中包括 最高效、最准确的神经网络架构和高级统计数据，可挖掘成像、临床和 在基线时确定的生物学TMJ OA标志物。在目标2中，我们建议开发新的数据分析工具， 评估各种机器学习和统计预测模型的性能，包括定制的- 高斯过程回归，极端提升树，多元变系数模型，Lasso，岭和 弹性网络，随机森林，pdfCluster，决策树和支持向量机。这种自动化解决方案 将利用新兴的计算技术来确定OA进展的风险指标， 颞下颌关节健康和疾病的队列。

ABSTRACT This application proposes the development of efficient web-based data management, mining, and analytics, to integrate and analyze clinical, biological, and high dimensional imaging data from TMJ OA patients. Based on our published results, we hypothesize that patterns of condylar bone structure, clinical symptoms, and biological mediators are unrecognized indicators of the severity of progression of TMJ OA. Efficiently capturing, curating, managing, integrating and analyzing this data in a manner that maximizes its value and accessibility is critical for the scientific advances and benefits that such comprehensive TMJ OA patient information may enable. High dimensional databases are increasingly difficult to process using on-hand database management tools or traditional processing applications, creating a continuing demand for innovative approaches. Toward this end, the DCBIA at the Univ. of Michigan has partnered with the University of North Carolina, the University of Texas MD Anderson Cancer Center and Kitware Inc. Through high-dimensional quantitative characterization of individuals with TMJ OA, at molecular, clinical and imaging levels, we will identify phenotypes at risk for more severe prognosis, as well as targets for future therapies. The proposed web-based system, the Data Storage for Computation and Integration (DSCI), will remotely compute machine learning, image analysis, and advanced statistics from prospectively collected longitudinal data on patients with TMJ OA. Due to its ubiquitous design in the web, DSCI software installation will no longer be required. Our long-term goal is to create software and data repository for Osteoarthritis of the TMJ. Such repository requires maintaining the data in a distributed computational environment to allow contributions to the database from multi-clinical centers and to share trained models for TMJ classification. In years 4 and 5 of the proposed work, the dissemination and training of clinicians at the Schools of Dentistry at the University of North Carol, Univ. of Minnesota and Oregon Health Sciences will allow expansion of the proposed studies. In Aim 1, we will test state-of-the-art neural network structures to develop a combined software module that will include the most efficient and accurate neural network architecture and advanced statistics to mine imaging, clinical and biological TMJ OA markers identified at baseline. In Aim 2, we propose to develop novel data analytics tools, evaluating the performance of various machine learning and statistical predictive models, including customized- Gaussian Process Regression, extreme boosted trees, Multivariate Varying Coefficient Model, Lasso, Ridge and Elastic net, Random Forest, pdfCluster, decision tree, and support vector machine. Such automated solutions will leverage emerging computing technologies to determine risk indicators for OA progression in longitudinal cohorts of TMJ health and disease.