A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters since different settings of the parameters result in significantly different outcomes. Therefore, we propose PointIso, to serve the necessity of an automated system for peptide feature detection that is able to find out the proper parameters itself, and is easily adaptable to different types of datasets. It consists of an attention based scanning step for segmenting the multi-isotopic pattern of peptide features along with charge and a sequence classification step for grouping those isotopes into potential peptide features. PointIso is the first point cloud based, arbitrary-precision deep learning network to address the problem and achieves 98% detection of high quality MS/MS identifications in a benchmark dataset, which is higher than several other widely used algorithms. Besides contributing to the proteomics study, we believe our novel segmentation technique should serve the general image processing domain as well.

中文翻译：

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PointIso：基于点云的深度学习模型，用于通过基于注意力的分割来检测LC-MS映射中的任意精密肽特征

一种发现疾病生物标志物的有前途的技术是通过液相色谱-串联质谱（LC-MS / MS）定量蛋白质组学技术测量多个生物流体样品中的相对蛋白质丰度。关键步骤涉及LC-MS图谱中的肽特征检测以及其电荷和强度。由于参数的不同设置导致明显不同的结果，因此现有的启发式算法存在参数不准确的问题。因此，我们提出PointIso，以服务于肽特征检测自动化系统的必要性，该系统能够找出合适的参数本身，并且很容易适应不同类型的数据集。它包括一个基于注意力的扫描步骤，用于分割肽特征的多同位素模式以及电荷；以及一个序列分类步骤，用于将这些同位素分组为潜在的肽特征。PointIso是第一个基于点云的任意精度深度学习网络，用于解决该问题，并在基准数据集中实现了98％的高质量MS / MS识别检测，这比其他几种广泛使用的算法要高。除了有助于蛋白质组学研究之外，我们相信我们新颖的分割技术也应服务于一般的图像处理领域。任意精度深度学习网络来解决该问题，并在基准数据集中实现98％的高质量MS / MS识别检测，这比其他几种广泛使用的算法要高。除了有助于蛋白质组学研究之外，我们相信我们新颖的分割技术也应服务于一般的图像处理领域。任意精度深度学习网络来解决该问题，并在基准数据集中实现98％的高质量MS / MS识别检测，这比其他几种广泛使用的算法要高。除了有助于蛋白质组学研究之外，我们相信我们新颖的分割技术也应服务于一般的图像处理领域。