Intelligent Aids for Proteomic Data Mining

蛋白质组数据挖掘的智能辅助工具

• 批准号: 7089794
• 负责人: Vanathi Gopalakrishnan
• 金额: $ 12.74万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7089794
• 关键词: bioinformatics; biomarker; biotechnology; computer assisted sequence analysis; computer program /software; computer system design /evaluation; data management; diagnosis design /evaluation; genetic disorder diagnosis; human data; mass spectrometry; proteomics

DESCRIPTION (provided by applicant): Primary purpose of this proposal is to provide the applicant with the means and structures for achieving two goals; (1) to develop intelligent computational aids for mining proteomic data accumulating from high throughput techniques like SELDI-TOF mass spectrometry; and (2) the long-term goal is to gain independence as a biomedical informatics researcher by developing methodological expertise in Bayesian methods and proteomic technologies. Applicant will obtain further instruction in probabilistic methods of data analysis; and she will receive education on proteomic technologies that are driving today's proteome research. Training will be provided through formal coursework, directed readings, seminars and conferences in addition to research directed by excellent mentors. Applicant's research project involves a novel combination of techniques for use in proteomic data analysis. Previous research has included the use of techniques such as genetic algorithms and neural networks for analysis of proteomic data. These techniques were not explicitly designed to take into account background and prior knowledge. Hypothesis of this project is that background knowledge and machine learning techniques can positively influence the selection of appropriate biomarkers from proteomic data, enabling efficient and accurate analysis of massive datasets arising from proteomic profiling studies. Therefore, this project will satisfy four aims: (1) development of a wrapper-based machine learning tool; (2) augment the tool with prior knowledge such as heuristic rules and relationships in the data; (3) use these features along with de-identified patient information as input to classification systems; and (4) evaluate existing techniques for interpreting tandem mass spectrometry (MS-MS or MS/MS) data, and propose, implement and evaluate a Bayesian method for identification of peptides and proteins indicated by the MS-MS spectrum.

描述（由申请人提供）：该提案的主要目的是为申请人提供实现两个目标的手段和结构； (1) 开发智能计算辅助工具，用于挖掘从 SELDI-TOF 质谱等高通量技术中积累的蛋白质组数据； （2）长期目标是通过发展贝叶斯方法和蛋白质组技术的方法学专业知识，获得作为生物医学信息学研究人员的独立性。申请人将获得数据分析概率方法的进一步指导；她将接受有关推动当今蛋白质组研究的蛋白质组技术的教育。除了优秀导师指导的研究之外，还将通过正式课程、定向阅读、研讨会和会议来提供培训。 申请人的研究项目涉及用于蛋白质组数据分析的新颖的技术组合。先前的研究包括使用遗传算法和神经网络等技术来分析蛋白质组数据。这些技术的设计并未明确考虑背景和先验知识。该项目的假设是，背景知识和机器学习技术可以对从蛋白质组数据中选择适当的生物标志物产生积极影响，从而能够对蛋白质组分析研究产生的大量数据集进行高效和准确的分析。因此，该项目将实现四个目标：（1）开发基于包装器的机器学习工具； (2) 用先验知识（例如启发式规则和数据中的关系）增强工具； (3) 使用这些特征以及去识别化的患者信息作为分类系统的输入； (4) 评估解释串联质谱（MS-MS 或 MS/MS）数据的现有技术，并提出、实施和评估用于鉴定 MS-MS 谱指示的肽和蛋白质的贝叶斯方法。