Open Mx: Multipurpose Software for Statistical Modeling

Open Mx：用于统计建模的多用途软件

• 批准号: 7501431
• 负责人: STEVEN M BOKER
• 金额: $ 26.38万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-26 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7501431
• 关键词: Addictive Behavior; Address; Algorithms; Architecture; Area; Astronomy; Behavior; Behavioral; Cardiovascular Diseases; Code; Communities; Complex; Computational Biology; Computer software; Computers; Data; Data Analyses; Data Set; Development; Discipline; Disease; Documentation; Educational workshop; Electrophysiology (science); Evolution; Funding; Future; Genetic; Genomics; Human; Individual; Individual Differences; Institutes; International; Investigation; Lead; Learning; Libraries; Magnetic Resonance Imaging; Maintenance; Malignant Neoplasms; Measurement; Methodology; Methods; Modeling; National Institute of Child Health and Human Development; National Institute of Drug Abuse; National Institute of Mental Health; Obesity; Pattern; Peer Review; Performance; Physics; Physiologic Monitoring; Play; Positioning Attribute; Process; Programming Languages; Psychopathology; Publications; Range; Research; Research Personnel; Role; Science; Scientist; Social Interaction; Source Code; Statistical Models; Structure; System; Systems Analysis; Techniques; Technology; Time; United States National Institutes of Health; Work; anticancer research; base; behavior measurement; cluster computing; computing resources; data management; design; genome wide association study; improved; multicore processor; neuroimaging; open source; parallel computing; programs; software development; tool; trait

DESCRIPTION (provided by applicant): Summary Statistical modeling plays a central role in a wide range of scientific investigations. Studies of complex traits and disorders such as addictive behaviors, psychopathology, cardiovascular disease, obesity, and cancer are now faced with a set of statistical challenges that require improved software. The challenges include: i) the difficulty of measuring behavioral traits; ii) the availability of technologies - such as magnetic resonance imaging, continuous physiological monitoring and microarrays - which generate extremely large amounts of data often with complex time-dependent patterning, and iii) increased sophistication in the statistical models used to analyze the data. The current project proposes to rewrite the popular Mx statistical package in order to address these challenges. Both the user specification of models and the algorithms used to ?t them will be significantly improved. The software will be i) split into modules that interoperate with the R statistical package, ii) released as open source so as to provide a stable path for future maintenance and development and iii) integrated with the VDL parallel work software. Grid/parallel computing and data management using VDL will provide significant speedup for processing large (up to multi-terabyte) data sets, through the use of analytical work that provide detailed provenance tracking and annotation of derived results. Revised algorithms for model and optimization will increase both the scope of the software and its performance. Both the code and its use will be documented and disseminated at national and international workshops. Studies of complex traits and disorders such as addictive behaviors, psychopathology, cardiovascular disease, obesity, and cancer are now faced with a set of statistical challenges that require improved software. The challenges include: i) the difficulty of measuring behavioral traits; ii) the availability of technologies - such as magnetic resonance imaging, continuous physiological monitoring and microarrays - which generate extremely large amounts of data often with complex time-dependent patterning; and iii) increased sophistication in the statistical models used to analyze the data. The current project proposes to develop software that uses massively parallel computing grids, "cyberinfrastructure", in order to address these challenges.

描述（由申请人提供）：摘要统计建模在广泛的科学研究中起着核心作用。对成瘾行为、精神病理学、心血管疾病、肥胖和癌症等复杂特征和疾病的研究现在面临着一系列统计挑战，需要改进软件。这些挑战包括：i）测量行为特征的难度; ii）技术的可用性-例如磁共振成像，连续生理监测和微阵列-这些技术产生了非常大量的数据，通常具有复杂的时间依赖性模式，以及iii）用于分析数据的统计模型的复杂性增加。目前的项目提议重写流行的Mx统计包，以应对这些挑战。无论是用户规格的模型和算法用于？将得到显著改善。该软件将i）拆分为与R统计包互操作的模块，ii）作为开源发布，以便为未来的维护和开发提供稳定的路径，以及iii）与VDL并行工作软件集成。网格/并行计算和数据管理使用VDL将提供显着的加速处理大型（高达数TB）的数据集，通过使用分析工作，提供详细的出处跟踪和注释的派生结果。修改后的模型和优化算法将增加软件的范围和性能。将在国家和国际讲习班上记录和传播该守则及其使用情况。对成瘾行为、精神病理学、心血管疾病、肥胖和癌症等复杂特征和疾病的研究现在面临着一系列统计挑战，需要改进软件。这些挑战包括：（i）测量行为特征的难度;（ii）磁共振成像、连续生理监测和微阵列等技术的可用性，这些技术产生的数据量非常大，往往具有复杂的时间依赖性模式;（iii）用于分析数据的统计模型越来越复杂。目前的项目提议开发使用大规模并行计算网格（“网络基础设施”）的软件，以应对这些挑战。