Machine learning analysis of tandem mass spectra

串联质谱的机器学习分析

• 批准号: 7365198
• 负责人: William Stafford Noble
• 金额: $ 60.25万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7365198
• 关键词: Abbreviations; Algorithms; Altretamine; Area; Authorship; Biochemical; Biological; Blast Cell; Calibration; Carbonyl Cyanide m-Chlorophenyl Hydrazone; Collection; Complement; Complex; Complex Mixtures; Computer Simulation; Computer software; Computers; Data; Databases; Devices; Diagnostic; Dissociation; FOLH1 gene; Genomics; Hand; Health; Health Sciences; Hour; Human; Knowledge; Learning; Machine Learning; Maps; Mass Spectrum Analysis; Methods; Modeling; Molecular; Peptide Fragments; Peptides; Performance; Post-Translational Protein Processing; Preparation; Principal Investigator; Procedures; Process; Protein Biochemistry; Proteins; Proteomics; Rate; Receiver Operating Characteristics; Research Personnel; Rest; Running; Sampling; Scientist; Score; Set protein; Silicon Dioxide; Source Code; Staging; Statistical Models; Techniques; Technology; Time; Today; Training; Work; computer based statistical methods; day; design; disease phenotype; expectation; improved; interest; markov model; mass spectrometer; model design; prognostic; programs; research study; small molecule; tandem mass spectrometry; task analysis; tool; user-friendly

DESCRIPTION (provided by applicant): Project summary: Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of molecules that comprise a complex biological sample. In the biological and health sciences, mass spectrometry is commonly used in a nigh-throughput fashion to identify proteins in a mixture. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We propose to apply techniques and tools from the field of machine learning to the analysis of mass spectrometry data. We will build computational models of peptide fragmentation within the mass spectrometer, as well as larger-scale models of the entire mass spectrometry process. Using these models, we will design and validate algorithms for identifying the set of proteins that best explain an observed set of spectra. Software implementations for all of the methods will be made publicly available in a user-friendly form. In practical terms, this software will enable scientists to more easily, efficiently and accurately analyze and understand their mass spectrometry data. Relevance: The applications of mass spectrometry and its promises for improvements of human health are numerous, including an increased understanding of disease phenotypes and the molecular mechanisms that underlie them, and vastly more sensitive and specific diagnostic and prognostic screens.

描述（由申请人提供）： 项目摘要：质谱法是蛋白质组学领域的核心技术，有望使科学家能够识别和量化构成复杂生物样品的全部分子。在生物和健康科学中，质谱分析通常以高通量方式用于识别混合物中的蛋白质。目前，此类实验的主要瓶颈是计算。现有的用于解释质谱的算法速度很慢并且无法识别给定光谱的大部分。我们建议将机器学习领域的技术和工具应用于质谱数据的分析。我们将在质谱仪内建立肽碎片的计算模型，以及整个质谱过程的更大规模的模型。使用这些模型，我们将设计和验证算法，用于识别最能解释观察到的一组光谱的蛋白质组。所有方法的软件实现将以用户友好的形式公开提供。实际上，该软件将使科学家能够更轻松、高效、准确地分析和理解他们的质谱数据。相关性：质谱的应用及其对改善人类健康的承诺有很多，包括加深对疾病表型及其分子机制的了解，以及更加灵敏和特异的诊断和预后筛查。