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（由适用提供）：新的具有成本效益的高通量基因组和成像技术已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新挑战。新的数据集是大型且经常多模式的，即测得的变量具有不同的类型：连续（OMICS，FMRI测量值），二进制（SNP，性别），数值（年龄，药物剂量），分类（疾病的家族史，疾病，转移的组织），orialtal阶段（tumor阶段，specer阶段，吸烟）。关键分析方面是发现变量之间的直接（因果关系）关联。由于许多原因，这很重要：它可用于分类，生物标志物选择，药物效应或疾病网络扰动的机械研究。过去已经使用了图形模型，但并未针对（a）多模式数据和（b）大数据集进行调整。该应用的目的是开发新的方法来识别因果或部分灾难性的网络，这些方法可用于支持和增强准确的疾病预测以及亚疾病分类，并有助于确定疾病分子机制的关键相互作用。我们将基于混合变量部分开发和测试新方法，部分灾难性图形（MVPCG）模型。评估将对合成和真实数据集进行，包括具有基因组，遗传和表观遗传学数据的并行数据集，临床信息和时间序列诊断图像数据。我们的中心假设是，对诊断患者数据的不同方式进行了综合的计算分析，可以识别临床和其他疾病相关特征之间的复杂关联和催化关系，从而有助于破译分子疾病机制。可交付成果将是（1）多模式生物医学和临床数据的整合和共分析的新图形方法； （2）一个针对MATLAB和R的新的，完整的已记录的软件包，可以在其他算法中无缝合并； （3）一个全新的全面支持的图形用户界面（GUI），以进一步将我们的方法传播给非计算机的用户； （4）关于转移性黑色素瘤患者的发病机理和预测特征的结果； （5）基于加速频谱受试者和神经型的预测特征的结果。如果成功的话，这个跨科学科的团队项目将产生以上可交付成果以外的积极影响，因为我们的方法的一般性使它们适合于研究任何疾病，并使它们在将来很容易整合到个性化的医学策略中，而当大量的高通量数据收集将成为所有医院的常规诊断程序。