Intelligent Aids for Proteomic Data Mining

描述（由申请人提供）：本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)开发基于SELDI-TOF质谱等高通量技术积累的蛋白质组学数据的智能计算辅助工具；(2)长期目标是通过发展贝叶斯方法和蛋白质组学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。申请人将在数据分析的概率方法方面获得进一步的指导；她将接受蛋白质组学技术方面的教育，这些技术正在推动今天的蛋白质组学研究。培训将通过正式课程、指导阅读、研讨会和会议以及由优秀导师指导的研究来提供。

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.