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Todays high-throughput molecular profiling technologies allow to routinely create large datasets providing detailed information about a given biological sample, e.g. about the concentrations of thousands contained proteins. A standard task in the context of precision medicine is to identify a set of biomarkers (e.g. proteins) from these datasets that can be used for disease diagnosis, prognosis or to monitor treatment response. However, finding good biomarker sets is still a challenging task due to the high dimensionality and complexity of the data and the often quite high noise level. In this work, we present an approach to this problem based on Deep Neural Networks (DNN) and a transfer learning strategy using simulation data. To allow interpretation of the results, we compare different approaches to analyze the learned DNN. Based on these interpretation approaches, we describe how to extract biomarker sets. Comparison of our method to a state-of-the-art L1-SVM approach shows that the new approach is able to find better biomarker sets for classification when small sets are desired. Compared to a state-of-the-art 1support vector machine ( 1-SVM) approach, our method achieves better results for the classification task when a small number of features are needed.

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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. Besides, 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. Next, based on the trained neural networks, biomarkers can be identified using the layer-wise relevance propagation technique. This enables detecting discriminating regions of the data and the design of more robust networks. We show that DLearnMS outperforms conventional LC-MS biomarker detection approaches in detecting fewer false positive peaks while maintaining a comparable amount of true positives peaks. Unlike other methods, no explicit preprocessing step is needed in DLearnMS.

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.