INTEGRATED BIOSTATISTICAL AND BIONFORMATIC ANALYSIS CORE (IBBAC)

综合生物统计学和生物信息学分析核心（IBBAC;发音为“eye-back”）的目标是 提供分析工具，数据分析，并获得最先进的工具，专业知识和领导力， 临床表型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）核心以及四个拟定项目。 IBBAC将利用现有的高维数据类型分析方法和工具 (e.g.,偏最小二乘法、支持向量机、聚类分析技术等）以及新 用于分析UCSD ACE生成的数据的方法和现有方法的扩展 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”） 谱系障碍（ASD）和/或发育迟缓（DD），并且与在 典型的发育中的儿童。房间隔缺损和发育不良鉴别“指纹”的生物学意义 从这些分析中出现的将是评估其有效性的主要考虑因素;即，一致性 这些指纹与该中心的主要动机假设，即出生后早期的大脑 过度生长是ASD/DD发病机制的标志。对新的多元数据分析方法的需求 神经精神病学和行为遗传学研究的类型提出了相当大的增长与 引入数据密集型技术，如大规模基因分型测定和基因表达 微阵列此外，信息密集型表型分析，如成像技术、多重 心理评估/详细的心理测量学检查，以及大规模的内在表型和/或认知 评估战略--可以用来补充基因组技术--已经被引入 这进一步需要适当的多变量分析方法。虽然有相当多的 在多参数生物过程的数学模型的开发中的研究（例如，基因 转录）以及基因组技术的数据挖掘/模式发现策略， 研究和实际实施，面向假设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 无论是单独还是组合。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，适用于从 作为UCSD ACE研究提案的一部分而产生的高维基因组和表型数据。 一些提出的数据分析方法建立和扩展了一些基本的多元 技术（例如，相似性和距离分析、多元回归和方差分量 模型）。

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).