Deep learning approaches for efficient interpretation of MS peptide spectra.

该项目旨在研究深度神经网络的应用，以识别原始MS光谱中可以与特定细胞表型和/或疾病状态可靠相关的信号。这可以潜在地识别具有临床价值的新的复杂生物标志物，并且还有助于通过避免原始MS数据的算法预处理来提高分析基于MS的蛋白质组学数据的效率，该算法预处理是耗时的并且引入误差源。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，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的能力。目标是比较不同的深度学习结构，以设计最适合识别大型复杂MS集合中的指示性特征的神经网络。基于不同粒度级别的蛋白质组学数据（例如MS 1光谱、MS 2光谱、肽ID、蛋白质）。应用所得到的最佳神经网络来解释来自健康和患病细胞分析的MS数据，从而识别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。

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.