标题： 二进制还是非二进制：质谱的替代表示方法 显示英文标题

标题： To Bin or not to Bin: Alternative Representations of Mass Spectra

摘要： 质谱分析，尤其是所谓的串联质谱分析，常用于评估样品的化学多样性。 产生的质谱碎片图谱是分子的表示，这些分子的结构可能尚未确定。 这带来了从质谱中实验确定或计算预测分子结构的挑战。 另一种选择是直接从光谱中预测分子性质或分子相似性。 已经提出了各种方法来嵌入质谱，以便在机器学习任务中进一步使用。 然而，这些方法需要对光谱进行预处理，这通常包括对峰进行分箱或子采样，主要理由是创建统一的向量大小并去除噪声。 在这里，我们研究了在下游机器学习任务之前对质谱进行分箱的两种替代方法，即基于集合和基于图的表示。 将这两种提出的表示方法分别与在回归任务上训练的集合变换器和图神经网络进行比较，我们表明它们都显著优于在分箱数据上训练的多层感知机。 显示英文摘要

摘要： Mass spectrometry, especially so-called tandem mass spectrometry, is commonly used to assess the chemical diversity of samples. The resulting mass fragmentation spectra are representations of molecules of which the structure may have not been determined. This poses the challenge of experimentally determining or computationally predicting molecular structures from mass spectra. An alternative option is to predict molecular properties or molecular similarity directly from spectra. Various methodologies have been proposed to embed mass spectra for further use in machine learning tasks. However, these methodologies require preprocessing of the spectra, which often includes binning or sub-sampling peaks with the main reasoning of creating uniform vector sizes and removing noise. Here, we investigate two alternatives to the binning of mass spectra before down-stream machine learning tasks, namely, set-based and graph-based representations. Comparing the two proposed representations to train a set transformer and a graph neural network on a regression task, respectively, we show that they both perform substantially better than a multilayer perceptron trained on binned data.