{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:44:56Z","timestamp":1742913896282,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030297251"},{"type":"electronic","value":"9783030297268"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29726-8_19","type":"book-chapter","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T20:04:56Z","timestamp":1566504296000},"page":"301-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning for Proteomics Data for Feature Selection and Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8923-326X","authenticated-orcid":false,"given":"Sahar","family":"Iravani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5590-5726","authenticated-orcid":false,"given":"Tim O. F.","family":"Conrad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"issue":"6928","key":"19_CR1","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1038\/nature01511","volume":"422","author":"R Aebersold","year":"2003","unstructured":"Aebersold, R., Mann, M.: Mass spectrometry-based proteomics. Nature 422(6928), 198 (2003)","journal-title":"Nature"},{"issue":"93","key":"19_CR2","first-page":"1","volume":"20","author":"M Alber","year":"2019","unstructured":"Alber, M., et al.: iNNvestigate neural networks!. J. Mach. Learn. Res. 20(93), 1\u20138 (2019)","journal-title":"J. Mach. Learn. Res."},{"issue":"7","key":"19_CR3","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)","journal-title":"PLoS ONE"},{"key":"19_CR4","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"issue":"1","key":"19_CR5","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1186\/s12859-017-1565-4","volume":"18","author":"TO Conrad","year":"2017","unstructured":"Conrad, T.O., et al.: Sparse proteomics analysis-a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data. BMC Bioinf. 18(1), 160 (2017)","journal-title":"BMC Bioinf."},{"key":"19_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/11875741_12","volume-title":"Computational Life Sciences II","author":"TOF Conrad","year":"2006","unstructured":"Conrad, T.O.F., et al.: Beating the noise: new statistical methods for detecting signals in MALDI-TOF spectra below noise level. In: Berthold, M.R., Glen, R.C., Fischer, I. (eds.) CompLife 2006. LNCS, vol. 4216, pp. 119\u2013128. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11875741_12"},{"issue":"3","key":"19_CR7","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"4","key":"19_CR8","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","volume":"52","author":"DL Donoho","year":"2006","unstructured":"Donoho, D.L., et al.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289\u20131306 (2006)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"Jul","key":"19_CR9","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(Jul), 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"19_CR10","doi-asserted-by":"publisher","first-page":"3812","DOI":"10.1158\/1078-0432.CCR-08-2701","volume":"15","author":"GM Fiedler","year":"2009","unstructured":"Fiedler, G.M., et al.: Serum peptidome profiling revealed platelet factor 4 as a potential discriminating peptide associated with pancreatic cancer. Clin. Cancer Res. 15(11), 3812\u20133819 (2009)","journal-title":"Clin. Cancer Res."},{"issue":"1","key":"19_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)","journal-title":"J. Stat. Softw."},{"issue":"17","key":"19_CR12","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1093\/bioinformatics\/bts447","volume":"28","author":"S Gibb","year":"2012","unstructured":"Gibb, S., Strimmer, K.: MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28(17), 2270\u20132271 (2012)","journal-title":"Bioinformatics"},{"issue":"19","key":"19_CR13","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1093\/bioinformatics\/btv334","volume":"31","author":"S Gibb","year":"2015","unstructured":"Gibb, S., Strimmer, K.: Differential protein expression and peak selection in mass spectrometry data by binary discriminant analysis. Bioinformatics 31(19), 3156\u20133162 (2015)","journal-title":"Bioinformatics"},{"key":"19_CR14","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR16","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Langs, G., Denk, H., Zatloukal, K., M\u00fcller, H.: Causability and explainabilty of artificial intelligence in medicine. Wiley Interdisc. Rev. Data Min. Knowl. Discovery, e1312 (2019)","DOI":"10.1002\/widm.1312"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1007\/978-3-319-46493-0_39","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Huang","year":"2016","unstructured":"Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646\u2013661. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_39"},{"key":"19_CR20","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456 (2015)"},{"key":"19_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-3-319-69775-8_4","volume-title":"Towards Integrative Machine Learning and Knowledge Extraction","author":"F Jayrannejad","year":"2017","unstructured":"Jayrannejad, F., Conrad, T.O.F.: Better interpretable models for proteomics data analysis using rule-based mining. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds.) Towards Integrative Machine Learning and Knowledge Extraction. LNCS (LNAI), vol. 10344, pp. 67\u201388. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-69775-8_4"},{"key":"19_CR22","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"8","key":"19_CR23","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1373\/clinchem.2004.047399","volume":"51","author":"J Kratzsch","year":"2005","unstructured":"Kratzsch, J., et al.: New reference intervals for thyrotropin and thyroid hormones based on national academy of clinical biochemistry criteria and regular ultrasonography of the thyroid. Clin. Chem. 51(8), 1480\u20131486 (2005)","journal-title":"Clin. Chem."},{"key":"19_CR24","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"1","key":"19_CR25","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1186\/1471-2164-10-S1-S3","volume":"10","author":"Q Liu","year":"2009","unstructured":"Liu, Q., et al.: Comparison of feature selection and classification for MALDI-MS data. BMC Genom. 10(1), S3 (2009)","journal-title":"BMC Genom."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Marrugal, \u00c1., Ojeda, L., Paz-Ares, L., Molina-Pinelo, S., Ferrer, I.: Proteomic-based approaches for the study of cytokines in lung cancer. Dis. Markers 2016 (2016)","DOI":"10.1155\/2016\/2138627"},{"key":"19_CR27","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807\u2013814 (2010)"},{"issue":"1","key":"19_CR28","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw."},{"key":"19_CR29","unstructured":"Samek, W., Montavon, G., Binder, A., Lapuschkin, S., M\u00fcller, K.R.: Interpreting the predictions of complex ml models by layer-wise relevance propagation. arXiv preprint arXiv:1611.08191 (2016)"},{"key":"19_CR30","unstructured":"Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 (2016)"},{"key":"19_CR31","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"19_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"19_CR33","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)"},{"key":"19_CR34","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)"},{"key":"19_CR35","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 3319\u20133328 (2017). JMLR.org"},{"key":"19_CR36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"19_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"issue":"2","key":"19_CR38","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Series B (Stat. Methodol.) 67(2), 301\u2013320 (2005)","journal-title":"J. Roy. Stat. Soc. Series B (Stat. Methodol.)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29726-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:40:12Z","timestamp":1710326412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29726-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030297251","9783030297268"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29726-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CD-MAKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canterbury","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}