SEI (BIO): Integrated analysis of heterogeneous genomic data for accurate prediction of gene function and interactions between proteins

SEI (BIO)：异质基因组数据的综合分析，以准确预测基因功能和蛋白质之间的相互作用

• 批准号: 0513552
• 负责人: Olga Troyanskaya
• 金额: $ 47.14万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0513552&HistoricalAwards=false
• 关键词: SEI; BIO; Integrated; analysis; heterogeneous

ABSTRACTThe objective of the proposed research is to develop a general and robust machine learning system for integrated analysis of high-throughput biological data for the purpose of prediction of gene function and protein-protein interactions. Achieving this goal requires addressing multiple challenges that include data heterogeneity, variable data quality, high noise levels in data, and a paucity of training samples. These challenges have prevented the successful application of traditional machine learning methods to diverse biological data. The research team will leverage diverse bioinformatics, machine learning, and biology expertise of the co-PIs and collaborators to develop accurate and effective approaches optimized for integrated analysis of genomic data. For prediction of protein-protein interactions, this investigation will focus on Bayesian approaches based on successful preliminary research. For gene function prediction, the focus will be on developing novel machine learning methods. These learning methods will use heterogeneous biological data as well as protein-protein interactions predicted by the system. The proposed research will lead to development of a general bioinformatics system that will utilize diverse large-scale biological data, including gene expression microarrays, physical and genetic interactions datasets, sequence and literature data, to produce an accurate map of protein-protein interactions and predictions of function for each of the proteins. This system will address the critical need in genomics to extract accurate biological information from disparate high-throughputdata sources, enabling the first step in accurate and comprehensive study of cellular processeson a whole-genome level. Additionally, the proposed analysis will provide genomics researchers with quantitative rankings of the relative reliability of high-throughput experimental technologies, thereby providing biologists with data on which high-throughput technologies are more accurate than others. A significant advantage of this plan is that the research team will work closely with biologists to evaluate the predictions and feed the information back into the investigation to further improve the system and the quality of the resulting predictions.The proposed system will provide predictions that will drive biological experimentation, enabling genome-wide annotation of unknown genes. The system will be publicly available to genomics researchers through its integration with the Saccharomyces Genome Database, a model organism database for yeast, and also via distribution of this integrated framework to other model databases. The interdisciplinary approach of this proposal will further the impact of advancedcomputer science on biology and will precipitate further interactions between the two fields, both through research and through interdisciplinary education.

摘要本研究的目的是开发一个通用的、鲁棒的机器学习系统，用于高通量生物数据的综合分析，以预测基因功能和蛋白质-蛋白质相互作用。实现这一目标需要解决多个挑战，包括数据异构性，可变的数据质量，数据中的高噪声水平以及训练样本的缺乏。这些挑战阻碍了传统机器学习方法在不同生物数据中的成功应用。该研究团队将利用合作PI和合作者的各种生物信息学，机器学习和生物学专业知识，开发针对基因组数据综合分析优化的准确有效的方法。对于蛋白质-蛋白质相互作用的预测，本研究将侧重于基于成功的初步研究的贝叶斯方法。对于基因功能预测，重点将是开发新的机器学习方法。这些学习方法将使用异质生物数据以及系统预测的蛋白质-蛋白质相互作用。拟议的研究将导致开发一个通用的生物信息学系统，该系统将利用各种大规模的生物数据，包括基因表达微阵列，物理和遗传相互作用数据集，序列和文献数据，以产生蛋白质-蛋白质相互作用的准确地图和每种蛋白质的功能预测。该系统将解决基因组学中从不同的高通量数据源中提取准确生物信息的关键需求，从而实现在全基因组水平上准确和全面研究细胞过程的第一步。此外，拟议的分析将为基因组学研究人员提供高通量实验技术相对可靠性的定量排名，从而为生物学家提供高通量技术比其他技术更准确的数据。该计划的一个显著优势是，研究团队将与生物学家密切合作，评估预测结果，并将信息反馈到调查中，以进一步改进系统和预测结果的质量。拟议的系统将提供预测，以推动生物实验，实现未知基因的全基因组注释。该系统将通过与酵母菌基因组数据库（酵母的模式生物数据库）的集成以及将该集成框架分发到其他模式数据库，向基因组学研究人员公开提供。该提案的跨学科方法将进一步推动先进的计算机科学对生物学的影响，并将通过研究和跨学科教育促进两个领域之间的进一步互动。