Deep learning approaches for efficient interpretation of MS peptide spectra.

用于有效解释 MS 肽谱的深度学习方法。

• 批准号: 2095730
• 金额: --
• 依托单位: University of Dundee
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2095730
• 关键词: Deep; learning; approaches; efficient; interpretation

The project is to investigate the application of deep neural networks to identify signals in raw MS spectra that can be reliably associated with specific cell phenotypes and/or disease states. This can potentially identify new complex biomarkers with clinical value and also help to improve the efficiency of analysing MS-based proteomics data by avoiding algorithmic preprocessing of the raw MS data, which is time consuming and introduces sources of error. The project will benefit from the availability of existing very large collections of highly annotated MS proteomics data in the Lamond Laboratory, the ability to generate new targeted data sets using cell models and clinical tissues and access to existing large-scale computational infrastructure.The objectives are to compare different deep learning structures to design neural networks best suited to identifying indicative features within large sets of complex MS-based proteomics data at different levels of granularity (e.g. MS1 spectra, MS2 spectra, peptide IDs, proteins). To apply the resulting optimal neural network to interpret MS data derived from analysis of healthy and diseased cells and thereby identify complex biomarkers diagnostic for specific cell phenotypes. Project provides training in Quantitative & Interdisciplinary Skills.

该项目旨在研究深度神经网络在原始质谱中识别信号的应用，这些信号可以可靠地与特定细胞表型和/或疾病状态相关联。这可以潜在地识别出具有临床价值的新的复杂生物标志物，也有助于提高分析基于MS的蛋白质组学数据的效率，避免对原始MS数据进行算法预处理，这既耗时又会引入错误来源。该项目将受益于Lamond实验室现有的大量高度注释的MS蛋白质组学数据的可用性，使用细胞模型和临床组织生成新的目标数据集的能力，以及访问现有的大规模计算基础设施。目的是比较不同的深度学习结构，以设计最适合在不同粒度水平（例如MS1光谱，MS2光谱，肽id，蛋白质）的大型复杂MS-based蛋白质组学数据集中识别指示性特征的神经网络。应用由此产生的最佳神经网络来解释来自健康和患病细胞分析的质谱数据，从而识别出诊断特定细胞表型的复杂生物标志物。项目提供定量和跨学科技能的培训。