RT Journal Article
SR Electronic
T1 An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.19.431935
DO 10.1101/2021.02.19.431935
A1 Sahar Iravani
A1 Tim O.F. Conrad
YR 2021
UL http://biorxiv.org/content/early/2021/06/13/2021.02.19.431935.abstract
AB Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework.Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks.Code availability The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMSCompeting Interest StatementThe authors have declared no competing interest.