Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics Data

MS 蛋白质组大数据的多模态机器学习和高性能计算策略

• 批准号: 10163880
• 负责人: Fahad Saeed
• 金额: $ 47.59万
• 依托单位: FLORIDA INTERNATIONAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10163880
• 关键词: Affect; Algorithms; Architecture; Big Data; Classification; Communities; Computational Technique; Computer Vision Systems; Data; Data Analyses; Deductibles; Development; Disease; Ensure; Galaxy; Goals; Health; High Performance Computing; Human; Investigation; Lead; Machine Learning; Mass Spectrum Analysis; Memory; Methods; Mission; Modeling; Names; Organism; Outcome; Peptides; Performance; Post-Translational Protein Processing; Process; Proteins; Proteomics; Public Health; Reproducibility; Research; Resolution; Sample Size; Sampling; Structure; System; Systems Biology; Techniques; Testing; Time; Training; United States National Institutes of Health; Work; algorithmic methodologies; base; cyber infrastructure; data acquisition; deep learning; deep learning algorithm; design; experimental study; high dimensionality; image processing; improved; indexing; innovation; insight; learning strategy; mass spectrometer; microbiome; multidimensional data; multimodality; novel; open data; open source; program dissemination; protein aminoacid sequence; protein biomarkers; reproductive; searchable database; simulation; success; tool; vector; virtual

Project Abstract/Summary Mass spectrometry (MS) data is high-dimensional data that is used for large-scale system biology proteomics. The current state of the art mass spectrometers can generate thousands of spectra from a single organism and experiment. This high-dimensional data is processed using database searches and denovo algorithms with varying degrees of success. The overarching objective of this study is to develop, test, integrate and evaluate novel image-processing and deep-learning algorithms that will allow us to deduce and identify reliable peptide sequences in a definitive and quantitative fashion. Our long-term goal is to improve on identification of MS based proteomics data using novel and scalable algorithms. The objective of this proposal is to investigate, design and implement machine-learning deep-learning algorithms for identification of peptides from MS data. Since deep-learning is very good at discovering intricate structures in high-dimensional data it will be ideal solution for discovering dark proteomics data and more accurate deduction of peptides. We predict that the integration of these methods, along with traditional numerical algorithms, will lead to a multimodal fusion-based approach for an optimized and accurate peptide deduction system for large-scale MS data. Further, we will design and implement data augmentation, memory-efficient indexing, and high-performance computing (HPC) to achieve these outcomes more efficiently with a shorter computational time. Therefore, this new line of investigation is significant since it has the potential to improve on long-stalled effort to increase accuracy, reliability and reproducibility of MS data analysis and search tools. The proximate expected outcome of this work is a novel set of deep-learning and image-processing tools which will allow much better insight in MS based proteomics data. The results will have an important positive impact immediately because these proposed research tasks will lay the groundwork to develop a new class of algorithms and will provide rapid, high-throughput, sensitive, and reproducible and reliable tools for MS based proteomics.

项目摘要/摘要 质谱（MS）数据是用于大规模系统生物学的高维数据 蛋白质组学。当前的艺术质谱仪可以从中产生数千个光谱 一个有机体和实验。使用数据库搜索处理此高维数据 和多个成功程度的Denovo算法。这项研究的总体目标是 开发，测试，整合和评估新型的图像处理和深度学习算法 允许我们以确定的定量方式推断和识别可靠的肽序列。我们的 长期目标是使用新颖和 可扩展算法。该建议的目的是调查，设计和实施 用于从MS数据鉴定肽的机器学习深度学习算法。自从 深度学习非常擅长在高维数据中发现复杂的结构 发现深蛋白质组学数据和更准确的肽的理想解决方案。我们 预测这些方法的集成以及传统的数值算法将领导 采用基于多模式融合的方法进行优化和准确的肽扣除 大型MS数据的系统。此外，我们将设计和实施数据增强， 记忆有效的索引和高性能计算（HPC）以实现这些结果 较短的计算时间更有效。因此，这一新的调查是 意义重大，因为它有可能改善长期努力以提高准确性，所以 MS数据分析和搜索工具的可靠性和可重复性。直观的预期 这项工作的结果是一组新颖的深度学习和图像处理工具，可以允许 更好地了解基于MS的蛋白质组学数据。结果将具有重要的积极 立即影响，因为这些提议的研究任务将为开发一个基础 新的算法类，并将提供快速，高通量，敏感和可重复的 可靠的基于MS的蛋白质组学的工具。