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: 7681648
• 负责人: NICHOLAS Joseph SCHORK
• 金额: $ 19.45万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7681648
• 关键词: Age; Anatomy; Back; Behavioral; 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; Disease regression; End Point; 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; data structure; endophenotype; interest; mathematical model; nerve stem cell; neuroimaging; neuropsychiatry; novel; postnatal; programs; response; tool

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).

综合生物统计和生物信息学分析核心(IBBAC；发音为Eyes-back)的目标是 提供分析工具、数据分析以及对最新工具、专业知识和领导力的访问 来自临床表型的数据的综合或联合分析：招募和临床 评估(B)和临床表型：治疗反应(C)核心以及四个拟议项目。 IBBAC将利用高维数据类型的现有分析方法和工具 (例如，偏最小二乘、支持向量机、聚类分析技术等。)以及小说 用于分析UCSD ACE生成的数据的现有途径的方法和扩展 研究人员。本研究的最终目的是确定一组独特的临床、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)，不同于 典型的--发展中的孩子。ASD和DD识别“指纹”的生物学意义 从这些分析中得出的结果将是评估其有效性的一个主要考虑因素；即 这些指纹带有中枢的主要激发假说，即出生后早期的大脑 过度生长是ASD/DD发病的标志。对新的多元数据分析方法的需求 所提出的类型的神经精神病学和行为遗传学研究随着 介绍大规模基因分型分析和基因表达等数据密集型技术 微阵列。此外，信息密集型表型分析，如成像技术，多重 行为评估/详细的心理测量检查，以及大规模的内表型和/或认知 评估策略--可以用来补充基因组技术--已经被引入 这就产生了对适当的多变量分析方法的进一步需求。尽管有相当多的 研究开发多参数生物过程的数学模型(例如，基因 转录)以及基因组技术的数据挖掘/模式发现策略，有更少的 面向假设的多元数据分析发展的研究与实际实施 考虑基因组和多重表型技术产生的信息的方法学 或者是单独的，或者是组合的。拟议的IBBAC活动将考虑开发、部署、 和解释新的多变量分析方法，适合从 作为拟议的加州大学圣迭戈分校ACE研究的一部分产生的高维基因组和表型数据。 一些拟议的数据分析方法建立在一些基本的多变量的基础上并进行了扩展 技术(例如，相似性和距离分析、多元回归和方差分量 模特)。