Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

View ORCID ProfileSahar Iravani, View ORCID ProfileTim O.F. Conrad
doi: https://doi.org/10.1101/2021.02.19.431935
Sahar Iravani
1department of Visual and Data-centric Computing, Zuse Institute of Berlin, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sahar Iravani
  • For correspondence: iravani@zib.de
Tim O.F. Conrad
2department of Visual and Data-centric Computing, Zuse Institute of Berlin, Germany and department of Mathematics and Computer Science in Freie Universität Berlin, Germany. E-mail:
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tim O.F. Conrad
  • For correspondence: conrad@zib.de
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

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/DlearnMS

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted June 13, 2021.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data
Sahar Iravani, Tim O.F. Conrad
bioRxiv 2021.02.19.431935; doi: https://doi.org/10.1101/2021.02.19.431935
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data
Sahar Iravani, Tim O.F. Conrad
bioRxiv 2021.02.19.431935; doi: https://doi.org/10.1101/2021.02.19.431935

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4383)
  • Biochemistry (9599)
  • Bioengineering (7092)
  • Bioinformatics (24864)
  • Biophysics (12615)
  • Cancer Biology (9957)
  • Cell Biology (14354)
  • Clinical Trials (138)
  • Developmental Biology (7949)
  • Ecology (12107)
  • Epidemiology (2067)
  • Evolutionary Biology (15989)
  • Genetics (10925)
  • Genomics (14743)
  • Immunology (9869)
  • Microbiology (23676)
  • Molecular Biology (9485)
  • Neuroscience (50871)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2683)
  • Physiology (4015)
  • Plant Biology (8657)
  • Scientific Communication and Education (1509)
  • Synthetic Biology (2397)
  • Systems Biology (6436)
  • Zoology (1346)