Matching statistics for development datasets.

A shallow neural network was used to embed lipid structures in a 2- or 3-dimensional space with the goal that structurally similar species have similar vectors. Tests on complete lipid databanks show that the method automatically produces distributions which follow conventional lipid classifications. The embedding is accompanied by the web-based software, Lipidome Projector. This displays user lipidomes as 2D or 3D scatterplots for quick exploratory analysis, quantitative comparison and interpretation at a structural level. Examples of published data sets were used for a qualitative comparison with literature interpretation.

本研究采用浅层神经网络将脂质结构嵌入二维或三维空间，旨在使结构相似的脂质物种具备相似的向量表征。针对完整脂质数据库的测试结果表明，该方法可自动生成贴合传统脂质分类体系的分布结果。该嵌入方法配套了网页端软件脂质组投影器（Lipidome Projector），可将用户的脂质组以二维或三维散点图形式可视化，用于快速探索性分析、定量对比及结构层面的解读阐释。研究采用已公开数据集作为示例，与已有文献中的解读结果开展定性对比。