Repository: Freie Universit├Ąt Berlin, Math Department

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

Iravani, Sahar and Conrad, T. O. F. (2021) An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20 (1). ISSN 1545-4963

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Official URL: https://ieeexplore.ieee.org/document/9676484

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 .

Item Type:Article
Subjects:Biological Sciences > Others in Biological Sciences > Applied Biological Sciences
Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group
ID Code:2451
Deposited By: Admin Administrator
Deposited On:18 Aug 2020 14:34
Last Modified:19 Jan 2024 07:37

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