SingleFrag

The following item could be divided into 3 parts:Neural networks trained to predict the presence of a peak in a specific MZ localization in a tandem mass spectrum. In the name of every file, the MZ position is written in the following way (mz*100).<b>ANN</b>: Artificial Neural Networks<b>GNN</b>: Graph Neural Networks<b>COM</b>: Networks that combine the predictive power of ANN and GNN<b>Mol2vecModel</b>: Contains a Mol2vec model trained to obtain a 300-dimensional vector from molecule SMILES.<b>modelData</b>:<i>AllMostFreqMolGeneral_rep_dades1.pickle: </i>file containing the number of peaks that are contained in every MZ bin from tandem mass spectra in the training set.<i>thresholdsANN.pickle: </i>threshold per each of the most frequented 1,000 MZ positions in the training set. If a prediction using an ANN model for a specific position is higher or equal to this value (for its specific MZ position), means that a peak in that bin is predicted.<i>thresholdsGNN.pickle: </i>same as above but for the GNN models.<i>thresholdsCOM.pickle: </i>same as above but for the COM models.Every data file is stored in a <i>.pickle </i>format, using Python 3.8.19.

搜集汇总

背景与挑战

背景概述

SingleFrag是一个用于代谢组学分析的数据集，主要包含训练好的神经网络模型（包括ANN、GNN和COM三类），用于预测串联质谱中特定MZ位置的峰值。数据集以.pickle格式存储，适用于Python 3.8.19环境，并提供了Mol2vec模型和相关阈值数据，支持代谢组学中的光谱预测研究。