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

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 蛋白质组学现有技术的质谱仪可以从质谱产生数千个光谱。 一个有机体和实验使用数据库搜索处理这些高维数据 和从头算法，具有不同程度的成功。本研究的总体目标是 开发、测试、集成和评估新的图像处理和深度学习算法， 允许我们以确定和定量的方式推断和鉴定可靠的肽序列。我们 长期目标是使用新的和新的方法改进基于MS的蛋白质组学数据的鉴定， 可扩展算法本提案的目标是调查、设计和实施 机器学习深度学习算法，用于从MS数据中识别肽。以来 深度学习非常擅长发现高维数据中的复杂结构， 发现暗蛋白质组学数据和更精确的肽推导的理想解决方案。我们 我预测，这些方法的集成，沿着与传统的数值算法，将导致 涉及用于优化和准确的肽推导的基于多模式融合的方法 大规模MS数据系统。此外，我们将设计和实施数据增强， 高效内存索引和高性能计算（HPC）来实现这些结果 更有效地利用更短的计算时间。因此，这条新的调查路线是 意义重大，因为它有可能改善长期停滞不前的努力，以提高准确性， MS数据分析和检索工具的可靠性和再现性。预期的近似值 这项工作的成果是一套新的深度学习和图像处理工具， 更好地了解基于MS的蛋白质组学数据。结果将有一个重要的积极意义 影响，因为这些拟议的研究任务将奠定基础，发展一个 新的算法类别，并将提供快速，高通量，灵敏，可重复和 MS蛋白质组学的可靠工具。

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.