{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:08:18Z","timestamp":1762409298254,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319591469"},{"type":"electronic","value":"9783319591476"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-59147-6_5","type":"book-chapter","created":{"date-parts":[[2017,5,17]],"date-time":"2017-05-17T01:04:08Z","timestamp":1494983048000},"page":"50-59","source":"Crossref","is-referenced-by-count":37,"title":["Deep Learning to Analyze RNA-Seq Gene Expression Data"],"prefix":"10.1007","author":[{"given":"D.","family":"Urda","sequence":"first","affiliation":[]},{"given":"J.","family":"Montes-Torres","sequence":"additional","affiliation":[]},{"given":"F.","family":"Moreno","sequence":"additional","affiliation":[]},{"given":"L.","family":"Franco","sequence":"additional","affiliation":[]},{"given":"J. M.","family":"Jerez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,5,18]]},"reference":[{"key":"5_CR1","unstructured":"Aiello, S., Kraljevic, T., Maj, P., with contributions from the H2O.ai team: h2o: R Interface for H2O (2016). https:\/\/CRAN.R-project.org\/package=h2o . R package version 3.10.0.8"},{"key":"5_CR2","first-page":"1089","volume":"5","author":"Y Bengio","year":"2004","unstructured":"Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of K-fold cross-validation. J. Mach. Learn. Res. 5, 1089\u20131105 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Cadieu, C., Hong, H., Yamins, D., Pinto, N., Ardila, D., Solomon, E., Majaj, N., DiCarlo, J.: Deep neural networks rival the representation of primate it cortex for core visual object recognition. PLoS Comput. Biol. 10(12) (2014)","DOI":"10.1371\/journal.pcbi.1003963"},{"issue":"1","key":"5_CR4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.media.2015.08.001","volume":"26","author":"F Ciompi","year":"2015","unstructured":"Ciompi, F., de Hoop, B., van Riel, S., Chung, K., Scholten, E., Oudkerk, M., de Jong, P., Prokop, M., van Ginneken, B.: Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Med. Image Anal. 26(1), 195\u2013202 (2015)","journal-title":"Med. Image Anal."},{"key":"5_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-642-40763-5_51","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"DC Cire\u015fan","year":"2013","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411\u2013418. Springer, Heidelberg (2013). doi: 10.1007\/978-3-642-40763-5_51"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Deng, L., Li, J., Huang, J.T., Yao, K., Yu, D., Seide, F., Seltzer, M.L., Zweig, G., He, X., Williams, J., Gong, Y., Acero, A.: Recent advances in deep learning for speech research at microsoft. In: ICASSP, pp. 8604\u20138608. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6639345"},{"issue":"1","key":"5_CR7","doi-asserted-by":"crossref","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\u201322 (2010)","journal-title":"J. Stat. Softw."},{"key":"5_CR8","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Society for Artificial Intelligence and Statistics (2010)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Le, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G., Dean, J., Ng, A.: Building high-level features using large scale unsupervised learning. In: International Conference on Machine Learning (2012)","DOI":"10.1109\/ICASSP.2013.6639343"},{"issue":"7553","key":"5_CR11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"12","key":"5_CR12","doi-asserted-by":"crossref","first-page":"I121","DOI":"10.1093\/bioinformatics\/btu277","volume":"30","author":"M Leung","year":"2014","unstructured":"Leung, M., Xiong, H., Lee, L., Frey, B.: Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), I121\u2013I129 (2014)","journal-title":"Bioinformatics"},{"issue":"1","key":"5_CR13","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1186\/1471-2105-12-323","volume":"12","author":"B Li","year":"2011","unstructured":"Li, B., Dewey, C.N.: RSEM: accurate transcript quantification from rna-seq data with or without a reference genome. BMC Bioinform. 12(1), 323 (2011)","journal-title":"BMC Bioinform."},{"issue":"1","key":"5_CR14","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR15","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs\/1409.3215 (2014)"},{"issue":"1","key":"5_CR16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 58(1), 267\u2013288 (1996)","journal-title":"J. Roy. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Urda, D., Aragon, F., Veredas, F., Franco, L., Jerez, J.M.: L1-regularization model enriched with biological knowledge. In: Proceedings of the 5th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2017) (2017)","DOI":"10.1007\/978-3-319-56148-6_52"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Wenger, Y., Galliot, B.: Rnaseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an illumina-454 hydra transcriptome. BMC Genomics 14(1) (2013)","DOI":"10.1186\/1471-2164-14-204"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-59147-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T05:00:49Z","timestamp":1719205249000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-59147-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319591469","9783319591476"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-59147-6_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}