On the feasibility of deep learning applications using raw mass spectrometry data.

ABSTRACT:

Summary

In recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and expert curation. Furthermore, as quantification is limited to a small set of peptides, potentially important biological information may be discarded. Here we demonstrate that deep learning can be used to learn discriminative features directly from raw MS data, eliminating hence the need of elaborate data processing pipelines. Using transfer learning to overcome sample sparsity, we exploit a collection of publicly available deep learning models already trained for the task of natural image classification. These models are used to produce

SUBMITTER: Cadow J 

PROVIDER: S-EPMC8275322 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature