Mass spectrometry-based proteomics, Nature, vol.422, issue.6928, p.198, 2003. ,
, innvestigate neural networks, vol.20, pp.1-8, 2019.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PloS one, vol.10, issue.7, p.130140, 2015. ,
, , 2015.
Sparse proteomics analysis-a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data, BMC bioinformatics, vol.18, issue.1, p.160, 2017. ,
Beating the noise: new statistical methods for detecting signals in maldi-tof spectra below noise level, International Symposium on Computational Life Science, pp.119-128, 2006. ,
Support-vector networks, Machine learning, vol.20, issue.3, pp.273-297, 1995. ,
Compressed sensing, IEEE Transactions on information theory, vol.52, issue.4, pp.1289-1306, 2006. ,
URL : https://hal.archives-ouvertes.fr/inria-00369486
Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, vol.12, pp.2121-2159, 2011. ,
Serum peptidome profiling revealed platelet factor 4 as a potential discriminating peptide associated with pancreatic cancer, Clinical Cancer Research, vol.15, issue.11, pp.3812-3819, 2009. ,
Regularization paths for generalized linear models via coordinate descent, Journal of statistical software, vol.33, issue.1, p.1, 2010. ,
Maldiquant: a versatile r package for the analysis of mass spectrometry data, Bioinformatics, vol.28, issue.17, pp.2270-2271, 2012. ,
Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis, Bioinformatics, vol.31, pp.3156-3162, 2015. ,
Deep sparse rectifier neural networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp.315-323, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00752497
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. ,
Improving neural networks by preventing co-adaptation of feature detectors, 2012. ,
Causability and explainabilty of artificial intelligence in medicine, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery p, p.1312, 2019. ,
, Densely connected convolutional networks, 2016.
Deep networks with stochastic depth, European Conference on Computer Vision, pp.646-661, 2016. ,
Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, pp.448-456, 2015. ,
Better interpretable models for proteomics data analysis using rule-based mining, Towards Integrative Machine Learning and Knowledge Extraction, pp.67-88, 2017. ,
Adam: A method for stochastic optimization, 2014. ,
New reference intervals for thyrotropin and thyroid hormones based on national academy of clinical biochemistry criteria and regular ultrasonography of the thyroid, Clinical chemistry, vol.51, issue.8, pp.1480-1486, 2005. ,
Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012. ,
Comparison of feature selection and classification for maldi-ms data, BMC genomics, vol.10, issue.1, 2009. ,
Proteomicbased approaches for the study of cytokines in lung cancer, Disease markers, p.2016, 2016. ,
Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), pp.807-814, 2010. ,
On the momentum term in gradient descent learning algorithms, Neural networks, vol.12, issue.1, pp.145-151, 1999. ,
Interpreting the predictions of complex ml models by layer-wise relevance propagation, 2016. ,
, Not just a black box: Learning important features through propagating activation differences, 2016.
, Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013.
, Very deep convolutional networks for large-scale image recognition, 2014.
, Smoothgrad: removing noise by adding noise, 2017.
, Striving for simplicity: The all convolutional net, 2014.
Axiomatic attribution for deep networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.3319-3328, 2017. ,
Going deeper with convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1-9, 2015. ,
Visualizing and understanding convolutional networks, European conference on computer vision, pp.818-833, 2014. ,
Visualizing and understanding convolutional networks, European conference on computer vision, pp.818-833, 2014. ,
Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.67, issue.2, pp.301-320, 2005. ,