INTEGRATIVE GRAPHICAL MODELS FOR LARGE MULTI-MODAL BIOMEDICAL DATA

大型多模态生物医学数据的集成图形模型

• 批准号: 9069093
• 负责人: PANAGIOTIS V BENOS
• 金额: $ 32.54万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9069093
• 关键词: Age; Algorithms; Attention; Binding; Bioinformatics; Biomedical Research; Brain; Classification; Clinical; Clinical Data; Clinical Research; Collaborations; Communities; Complement; Complex; Computer Analysis; Computer software; Computers; Consensus; Data; Data Analyses; Data Collection; Data Set; Databases; Diagnostic; Diagnostic Imaging; Diagnostic Neoplasm Staging; Diagnostic Procedure; Disease; Effectiveness; Epigenetic Process; Evaluation; Explosion; Family history of; Functional Magnetic Resonance Imaging; Future; Gender; Gene Expression; Genes; Genetic; Genomic medicine; Genomics; Graph; Hereditary Disease; Hospitals; Human; Imaging technology; Individual; Internet; Knowledge; Learning; Libraries; Life; Measurement; Measures; Medical center; Medicine; Metastatic Melanoma; Methodology; Methods; Modality; Modeling; Neoplasm Metastasis; Pathogenesis; Patients; Pharmaceutical Preparations; Phenotype; Procedures; Reading; Research; Research Personnel; Science; Series; Smoking; Speed; Stream; Structure; Technology; Testing; Time; Tissues; Training; Tumor stage; Universities; Validation; base; biomarker selection; brain research; causal model; clinical Diagnosis; clinical care; computer science; cost effective; disease classification; disease diagnosis; dosage; follow-up; graphical user interface; human disease; improved; innovation; learning materials; learning strategy; melanoma; novel; outcome forecast; outreach; outreach program; performance tests; personalized medicine; programs; repository; research study; symposium; tool

 DESCRIPTION (provided by applicant): The new cost-effective high-throughput genomic and imaging technologies have revolutionized the field of clinical diagnosis and research, but they have also created a number of new and significant challenges. The new datasets are large and frequently multi-modal, i.e. the measured variables are of different types: continuous (omics, fMRI measurements), binary (SNPs, gender), numerical (age, drug dosage), categorical (family history of disease, tissue of metastasis), ordinal (tumor stage, smoking). A key analysis aspect is to discover the direct (causal) associations between variables. This is important for many reasons: it can be used for classification, biomarker selection, drug effect, or for mechanistic studies of network perturbations in disease. Graphical models have been used in the past but they are not tuned for (a) multi-modal data and (b) large datasets. The objective of this application is to develop novel methodologies that will identify causal or partially causal networks, which can be used to support and enhance accurate disease prediction, and sub disease classification and help identify key interactions of the molecular mechanisms of diseases. We will develop and test new methodologies based on mixed variable partially causal graphical (MVPCG) models. Evaluation will be done on synthetic and real datasets, including parallel datasets with genomic, genetic and epigenetic data, clinical information and time series diagnostic image data. Our central hypothesis is that an integrative, computational analysis of different modalities of diagnostic patient data can identify complex associations and causal relations between clinical and other disease relevant features and thus help decipher the molecular disease mechanisms. The deliverables will be (1) new graphical approaches for integration and co-analysis of multi-modal biomedical and clinical data; (2) a new, fully documented software package for MatLab and R that can be seamlessly incorporated in other algorithms; (3) a new fully supported graphical user interface (GUI) to further disseminate our methodologies to non computer-savvy users; (4) results on the pathogenesis and predictive features of metastatic melanoma patients; and (5) results on predictive features of autistic spectrum subjects and neurotypicals. If successful, this cross-disciplinary team project will have a positive impact beyond the above deliverables, since the generality of our approaches makes them suitable for studying of any disease and makes them easily integratable into personalized medicine strategies in the future when massive high-throughput data collection will become a routine diagnostic procedure in all hospitals.

 描述（由申请人提供）：新的具有成本效益的高通量基因组和成像技术彻底改变了临床诊断和研究领域，但它们也带来了许多新的重大挑战。新的数据集很大且通常是多个的。 -模态，即测量变量具有不同类型：连续（组学、功能磁共振成像测量）、二元（SNP、性别）、数值（年龄、药物剂量）、分类（疾病家族史、组织）一个关键的分析方面是发现变量之间的直接（因果）关联，原因有很多：它可用于分类、生物标志物选择、药物效应或机制。过去曾使用过图形模型来研究疾病中的网络扰动，但它们并未针对 (a) 多模式数据和 (b) 大型数据集进行调整。部分因果关系我们将开发和测试基于混合变量部分因果图（MVPCG）模型的新方法。我们的中心假设是，对不同模式的诊断患者数据进行综合计算分析可以识别复杂的关联。以及临床和临床之间的因果关系其他疾病相关特征，从而帮助破译分子疾病机制，交付成果将是（1）用于整合和共同分析多模式生物医学和临床数据的新图形方法；（2）一个新的、完整记录的 MatLab 软件包。和 R，可以无缝地融入其他算法；（3）一个完全支持的新图形用户界面（GUI），以进一步向不懂计算机的用户传播我们的方法；（4）关于转移性黑色素瘤的发病机制和预测特征的结果； （5）自闭症谱系受试者和神经典型患者的预测特征结果如果成功，这个跨学科团队项目将产生超出上述成果的积极影响，因为我们的方法的通用性使它们适合研究任何疾病。当大量高通量数据收集将成为所有医院的常规诊断程序时，使它们能够轻松地集成到未来的个性化医疗策略中。