Integration of fMRI imaging, genomics, network and biological knowledge

整合功能磁共振成像、基因组学、网络和生物知识

• 批准号: 8985308
• 负责人: YU-PING WANG
• 金额: $ 49.03万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-24 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8985308
• 关键词: Accounting; Area; Biological; Biological Markers; Biomedical Engineering; Bipolar Disorder; Brain; Characteristics; Classification; Clinical; Communities; Complex; Computer software; Data; Data Analyses; Databases; Detection; Diagnosis; Disease; Functional Magnetic Resonance Imaging; Genes; Genetic; Genetic Epistasis; Genetic Techniques; Genomic approach; Genomics; Gifts; Goals; Group Structure; Image; Joints; Journals; Knowledge; Lasso; Medical; Mental disorders; Methods; Modality; Modeling; Multimodal Imaging; Nature; Paper; Patients; Pattern; Psyche structure; Psychiatrist; Research; Sample Size; Sampling; Schizophrenia; Scientist; Single Nucleotide Polymorphism; Structure; Subgroup; Symptoms; Testing; Unipolar Depression; Validation; Variant; Work; Yin; base; computerized data processing; data integration; disease diagnosis; experience; genetic information; imaging modality; improved; innovation; multidisciplinary; novel; novel strategies; open source; outcome forecast; protein protein interaction; prototype; public health relevance; risk variant; success; symposium; tool; user-friendly

 DESCRIPTION (provided by applicant): Imaging genomics has emerged recently as a very promising and active research area by combining imaging and genomics approaches for comprehensive and systematic diagnosis of complex diseases. Utilizing multiscale and multimodal imaging genetic techniques such as fMRI imaging and SNP arrays, complementary information can be fused for better diagnosis and prognosis of diseases. However, fusion of these heterogeneous data has been extremely difficult. Most current approaches still analyze these data separately or with simple pairwise correlations and regression; many significant challenges exist: 1) Many available biological knowledge databases (e.g., protein-protein interactions (PPI)) contain rich and useful information but they have not been incorporated into data fusion. 2) Currently available data fusion approaches usually overlook the inter-correlations between imaging and genetics data, especially the interaction patterns within/between each type of data. 3) Imaging genetic data usually have smaller sample size but contain greater number of features. Many current approaches fail to consider these specific features and become ineffective in processing these data. To this end, the goal of this project is to tackle above significant challenges by developing innovative data integration approaches for the detection of novel biomarkers and use them for the identification of genes (modules) and improved diagnosis of complex diseases. We have assembled a multidisciplinary team including bioinformatician and biomedical engineer, imaging scientist, statistical geneticist, clinical psychiatrist, and medical informatician with complementary and synergistic expertise and experiences. We have collaborated productively and our preliminary results have demonstrated promising results for improved diagnosis of disease with integrative approaches. Building upon this success, we plan to accomplish the following specific aims: to develop novel computational approaches to correlate and integrate fMRI imaging with genomic data while incorporate biological knowledge and their interaction networks for the detection of biomarkers; and to apply/validate the detected biomarkers for the identification of risk genes/gene modules and for the improved diagnosis of subtle patient subgroups. Through this project, we will deliver a set of powerful sparse model based methods for imaging and genomic data fusion, especially by incorporating interaction networks and biological knowledge, which are often overlooked by current approaches. In addition, we will disseminate the developed methods via an open source software toolbox so that this project can have a broad and sustainable impact. We use mental disorders as a prototype for the validation but the developed models and tools can be applicable to studies of multiple other diseases.

 描述（由申请人提供）：通过结合成像和基因组学方法对复杂疾病进行全面和系统的诊断，成像基因组学最近已成为一个非常有前途和活跃的研究领域。利用功能磁共振成像和 SNP 阵列等多尺度和多模态成像遗传技术，可以融合互补信息，以更好地诊断和预测疾病。然而，这些异构数据的融合极其困难。目前大多数方法仍然单独分析这些数据，或者使用简单的成对相关性和回归分析；存在许多重大挑战：1）许多可用的生物知识数据库（例如蛋白质-蛋白质相互作用（PPI））包含丰富且有用的信息，但尚未纳入数据融合。 2）目前可用的数据融合方法通常忽略成像和遗传学数据之间的相互关联，特别是每种类型数据内/之间的交互模式。 3）成像遗传数据通常样本量较小，但包含更多特征。当前的许多方法未能考虑这些特定特征，并且在处理这些数据时变得无效。 为此，该项目的目标是通过开发创新的数据集成方法来检测新型生物标志物，并将其用于基因（模块）的识别和改进复杂疾病的诊断，以应对上述重大挑战。我们组建了一支多学科团队，包括生物信息学家和生物医学工程师、影像科学家、统计遗传学家、临床精神病学家和医学信息学家，具有互补和协同的专业知识和经验。我们进行了富有成效的合作，我们的初步结果表明，通过综合方法改进疾病诊断取得了有希望的结果。在此成功的基础上，我们计划实现以下具体目标：开发新的计算方法，将功能磁共振成像与基因组数据关联和整合，同时结合生物知识及其相互作用网络来检测生物标志物；应用/验证检测到的生物标志物，以识别风险基因/基因模块，并改进对细微患者亚组的诊断。 通过这个项目，我们将提供一套 基于强大的稀疏模型的成像和基因组数据融合方法，特别是通过结合交互网络和生物知识，这通常被当前的方法所忽视。此外，我们将通过开源软件工具箱传播开发的方法，以便该项目能够产生广泛且可持续的影响。我们使用精神障碍作为验证原型，但开发的模型和工具可以适用于多种其他疾病的研究。