Integrated SNP, gene expression and proteomic analysis

描述（由申请人提供）：该项目将开发一个软件套件（GeneSide），该软件套件包括用于数据管理、可视化、机器学习、统计分析和来自大规模生物平台（基因表达、SNP和蛋白质组学）的数据的生物学解释的工具，以及辅助临床、人口统计学、流行病学、实验室和结果数据。 新的大规模生物技术和强大的生物信息学挑战：有效挖掘生物和临床相关信息需要计算机科学家，统计学家，数学家，生物学家和临床医生的协调贡献。然而，潜在的益处也是实质性的，如基因表达微阵列的快速增长的使用所证明的。随着科学家开始整合SNP/蛋白质组数据和基因表达数据，复杂性和回报将急剧增加，并且将需要新一代软件来应对这一挑战。 基因组学将包括预处理和标准化原始数据以减少噪音的算法，将提供灵活，交互和直观的图形界面，将支持无监督和监督分类，以及使用适当的统计方法和经过验证的机器学习算法进行二分或生存结果预测，并将广泛的生物信息集成到软件中，并可直接从Web资源获得。

DESCRIPTION (provided by applicant): This project will see development of a software suite (Genetrix) comprised of tools for data management, visualization, machine learning, statistical analysis and biologic interpretation of data from the large-scale biological platforms (gene expression, SNP and proteomics), in conjunction with ancillary clinical, demographic, epidemiological, laboratory and outcome data. The bioinformatics challenges of the new large-scale biotechnologies and formidable: efficient mining of biologically- and clinically-relevant information requires coordinated contributions from computer scientists, statisticians, mathematicians, biologists and clinicians. The potential benefits however, are also substantial as evidenced by the rapidly growing use of gene expression rnicroarrays. The complexities, and payoffs, will increase dramatically as scientists begin to integrate SNP/proteomic data and gene expression data, and there will be demand for a new generation of software to meet this challenge. Genetrix will include algorithms to pre-process and normalize raw data to reduce noise, will provide a flexible, interactive and intuitive graphical interface, will support unsupervised and supervised for classification, and for dichotomous or survival outcome prediction, using appropriate statistic methods as well as proven machine learning heuristics, and will have extensive biological information integrated into the software, and available directly from Web resources.