3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data.

ABSTRACT:

Motivation

Liquid chromatography coupled with high-resolution mass spectrometry is widely used in composition profiling in untargeted metabolomics research. While retaining complete sample information, mass spectrometry (MS) data naturally have the characteristics of high dimensionality, high complexity, and huge data volume. In mainstream quantification methods, none of the existing methods can perform direct 3D analysis on lossless profile MS signals. All software simplify calculations by dimensionality reduction or lossy grid transformation, ignoring the full 3D signal distribution of MS data and resulting in inaccurate feature detection and quantification.

Results

On the basis that the neural network is effective for high-dimensional data analysis and can discover implic

SUBMITTER: Wang R 

PROVIDER: S-EPMC10181839 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature