Abstract
Deep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact that this family of models are known to work very well in big datasets with lots of samples available, just the opposite scenario typically found in biomedical areas. In this work, a first approximation on the use of deep learning for the analysis of RNA-Seq gene expression profiles data is provided. Three public cancer-related databases are analyzed using a regularized linear model (standard LASSO) as baseline model, and two deep learning models that differ on the feature selection technique used prior to the application of a deep neural net model. The results indicate that a straightforward application of deep nets implementations available in public scientific tools and under the conditions described within this work is not enough to outperform simpler models like LASSO. Therefore, smarter and more complex ways that incorporate prior biological knowledge into the estimation procedure of deep learning models may be necessary in order to obtain better results in terms of predictive performance.
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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
Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of K-fold cross-validation. J. Mach. Learn. Res. 5, 1089–1105 (2004)
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)
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), 195–202 (2015)
Cireşan, 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. 411–418. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_51
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. 8604–8608. IEEE (2013)
Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010)
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)
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)
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)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Leung, M., Xiong, H., Lee, L., Frey, B.: Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), I121–I129 (2014)
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)
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), 1929–1958 (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR abs/1409.3215 (2014)
Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 58(1), 267–288 (1996)
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)
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)
Acknowledgements
The authors acknowledge support through grants TIN2014-58516-C2-1-R from MICINN-SPAIN which include FEDER funds, and from ICE Andalucía TECH (Spain) through a postdoctoral fellowship.
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Urda, D., Montes-Torres, J., Moreno, F., Franco, L., Jerez, J.M. (2017). Deep Learning to Analyze RNA-Seq Gene Expression Data. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_5
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