INTEGRATED BIOSTATISTICAL AND BIONFORMATIC ANALYSIS CORE (IBBAC)

集成生物统计和生物信息学分析核心 (IBBAC)

• 批准号: 8117639
• 负责人: NICHOLAS Joseph SCHORK
• 金额: $ 20.87万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8117639
• 关键词: Age; Anatomy; Back; Behavioral; Behavioral Genetics; Bioinformatics; Biological; Biological Assay; Biological Process; Biological Response Modifier Therapy; Biometry; Brain; Child; Clinical; Clinical Treatment; Clinical assessments; Cluster Analysis; Cognitive; Complement; Computer software; Computers; Data; Data Analyses; Databases; Development; Developmental Delay Disorders; Diagnosis; Disease; Evolution; Eye; Fingerprint; Flowcharts; Future; Gender; Gene Expression; Genes; Genetic; Genetic Research; Genetic Transcription; Genetic Variation; Genomics; Genotype; Goals; Human; Image; Imaging technology; Individual; Internet; Knowledge; Language; Leadership; Least-Squares Analysis; Machine Learning; Meta-Analysis; Methodology; Methods; Modeling; Multivariate Analysis; Online Systems; Pathogenesis; Pathway interactions; Pattern; Phenotype; Predisposition; Psychometrics; Research; Research Personnel; Running; Sampling; Schedule; Secure; Speed; Structure; Structure-Activity Relationship; System; Techniques; Technology; Work; autism spectrum disorder; base; clinical phenotype; data mining; endophenotype; interest; mathematical model; middleware; nerve stem cell; neuroimaging; neuropsychiatry; novel; postnatal; programs; tool; treatment response; web site

The goal of the Integrated Biostatistics and Bioinformatics Analysis Core (IBBAC; pronounced "eye-back") is to provide analysis tools, data analyses, and access to state-of-the-field tools, expertise, and leadership for the integrated or combined analysis of data arising from the Clinical Phenotype: Recruitment and Clinical Assessment (B) and Clinical Phenotype: Treatment Response (C) Cores as well as the four proposed projects. The IBBAC will take advantage of both existing analysis methods and tools for high-dimensional data types (e.g., partial least squares, support vector machines, cluster analysis techniques, etc.) as well as novel methods and extensions of existing approaches for analyzing the data generated by the UCSD ACE researchers. The ultimate aim of this research is to identify a unique set of clinical, subclinical (e.g., imaging-based phenotypes), and genomic endpoints (or "fingerprints") that are correlated with Autism Spectrum Disorder (ASD) and/or Developmental Delay (DD) and are distinct from features found in typically-developing children. The biological-meaning of the identified "fingerprints" of ASD and DD emerging from these analyses will be a major consideration in assessing their validity; i.e., consistency of these fingerprints with the main motivating hypothesis of the center, which is that early postnatal brain overgrowth is the hallmark of ASD/DD pathogenesis. The need for novel multivariate data analysis methods in neuropsychiatric and behavioral genetics research of the type proposed has grown considerably with the introduction of data intensive technologies such as large-scale genotyping assays and gene expression microarrays. In addition, information-intensive phenotyping assays such as imaging technologies, multiplex behavorial assessments/elaborate psychometric exams, and large-scale endophenotype and/or cognitive assessment strategies - that could be used to complement genomic technologies - have been introduced which create further needs for appropriate multivariate analysis methods. Although there is considerable research in the development of mathematical models of multiparameter biological processes (e.g., gene transcription) as well as data mining/pattern discovery strategies for genomic technologies, there is less research on, and actual implementation of, the development of hypothesis-oriented multivariate data analysis methodologies that consider the information produced by genomic and multiplex phenotyping technologies either in isolation or in combination. The proposed IBBAC activity will consider the development, deployment, and interpretation of novel multivariate analysis methods appropriate for drawing meaningful inferences from the high-dimensional genomic and phenotypic data generated as part of the proposed UCSD ACE research. Some of the proposed data analysis methodologies build off and extend a few fundamental multivariate techniques (e.g., the analysis of similarity and distance, multivariate regression, and variance component models).

综合生物统计学和生物信息学分析核心（IBBAC；发音为“靠眼”）的目的是 提供分析工具，数据分析以及访问最新的工具，专业知识和领导才能 临床表型引起的数据的综合或组合分析：招募和临床 评估（B）和临床表型：治疗反应（C）核心以及四个提议的项目。 IBBAC将利用现有的分析方法和高维数据类型的工具 （例如，部分最小二乘，支持向量机，集群分析技术等）以及新颖 现有方法的方法和扩展，用于分析UCSD ACE生成的数据 研究人员。这项研究的最终目的是确定一套独特的临床，亚临床（例如， 基于成像的表型）和与自闭症相关的基因组终点（或“指纹”） 频谱障碍（ASD）和/或发育延迟（DD），与发现的特征不同 通常是发展的孩子。 ASD和DD已鉴定出的“指纹”的生物屈服 从这些分析中出现将是评估其有效性的主要考虑因素；即，一致性 这些指纹具有主要激励假设的中心的指纹，即早期产后大脑 过度生长是ASD/DD发病机理的标志。需要新颖的多元数据分析方法 所提出类型的神经精神和行为遗传学研究已随着 引入数据密集型技术，例如大型基因分型测定和基因表达 微阵列。此外，信息密集型表型测定法，例如成像技术，多路复用 遗嘱评估/详尽的心理测试以及大规模的内表型和/或认知 已经引入了评估策略 - 可以用来补充基因组技术 - 已被引入 这为适当的多元分析方法创造了进一步的需求。虽然有很多 多参数生物学过程的数学模型的开发研究（例如基因 转录）以及基因组技术的数据挖掘/模式发现策略，较少 研究和实际实施，以假设为导向的多元数据分析的发展 考虑基因组和多重表型技术产生的信息的方法论 孤立或组合。拟议的IBBAC活动将考虑开发，部署， 并解释适用于从中汲取有意义推论的新型多元分析方法 作为拟议的UCSD ACE研究的一部分生成的高维基因组和表型数据。 一些提出的数据分析方法构建并扩展了一些基本的多变量 技术（例如，相似性和距离的分析，多元回归和方差成分 模型）。