Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning, Nature biotechnology, vol.33, issue.8, pp.831-838, 2015. ,
Support vector machines and kernels for computational biology, PLoS Computational Biology, vol.4, issue.10, 2008. ,
Invariance and stability of deep convolutional representations, Advances in Neural Information Processing Systems (NIPS), pp.6210-6220, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01630265
Parseval networks: Improving robustness to adversarial examples, International Conference on Machine Learning, 2017. ,
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons, BMC Genomics, vol.17, issue.1, p.754, 2016. ,
Understanding the difficulty of training deep feedforward neural networks, Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp.249-256, 2010. ,
Quantifying similarity between motifs, Genome biology, vol.8, issue.2, p.24, 2007. ,
Motif kernel generated by genetic programming improves remote homology and fold detection, BMC bioinformatics, vol.8, issue.1, p.23, 2007. ,
Comparing biases for minimal network construction with backpropagation, Advances in Neural Information Processing Systems (NIPS), pp.177-185, 1989. ,
Amino acid substitution matrices from protein blocks, Proceedings of the National Academy of Sciences, vol.89, pp.10915-10919, 1992. ,
Fast model-based protein homology detection without alignment, Bioinformatics, vol.23, issue.14, pp.1728-1736, 2007. ,
A discriminative framework for detecting remote protein homologies, Journal of Computational Biology (JCB), vol.7, issue.1-2, pp.95-114, 2000. ,
Integrative deep models for alternative splicing, Bioinformatics, vol.33, issue.14, pp.274-282, 2017. ,
Virtual chip-seq: Predicting transcription factor binding by learning from the transcriptome. bioRxiv, 2018. ,
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks, Genome Research, vol.26, issue.7, pp.990-999, 2016. ,
Sequential regulatory activity prediction across chromosomes with convolutional neural networks, Genome research, 2018. ,
Systematic discovery and characterization of regulatory motifs in encode tf binding experiments, Nucleic acids research, vol.42, issue.5, pp.2976-2987, 2013. ,
Adam: A method for stochastic optimization, 2015. ,
Denoising genome-wide histone chip-seq with convolutional neural networks, Bioinformatics, vol.33, issue.14, pp.225-233, 2017. ,
Profile-based string kernels for remote homology detection and motif extraction, Journal of bioinformatics and computational biology, vol.3, issue.03, pp.527-550, 2005. ,
Scalable algorithms for string kernels with inexact matching, Advances in neural information processing systems, pp.881-888, 2009. ,
Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks, pp.254-265, 2017. ,
Backpropagation applied to handwritten zip code recognition, Neural computation, vol.1, issue.4, pp.541-551, 1989. ,
Mismatch String Kernels for SVM Protein Classification, Advances in Neural Information Processing Systems 15, 2003. ,
DOI : 10.1093/bioinformatics/btg431
URL : https://academic.oup.com/bioinformatics/article-pdf/20/4/467/476867/btg431.pdf
Fast string kernels using inexact matching for protein sequences, Journal of Machine Learning Research, vol.5, pp.1435-1455, 2004. ,
The spectrum kernel: A string kernel for svm protein classification, Pacific Symposium on Biocomputing, vol.7, pp.566-575, 2002. ,
Mismatch string kernels for discriminative protein classification, Bioinformatics, vol.20, issue.4, pp.467-476, 2004. ,
Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships, Journal of computational biology, vol.10, issue.6, pp.857-868, 2003. ,
On the limited memory bfgs method for large scale optimization, Mathematical Programming, vol.45, issue.1, pp.503-528, 1989. ,
End-to-end kernel learning with supervised convolutional kernel networks, Advances in Neural Information Processing Systems (NIPS), pp.1399-1407, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01387399
Convolutional kitchen sinks for transcription factor binding site prediction, 2017. ,
Random features for large-scale kernel machines, Adv. in Neural Information Processing Systems (NIPS), pp.1177-1184, 2008. ,
Profile-based direct kernels for remote homology detection and fold recognition, Bioinformatics, vol.21, issue.23, pp.4239-4247, 2005. ,
Protein homology detection using string alignment kernels, Bioinformatics, vol.20, issue.11, pp.1682-1689, 2004. ,
DOI : 10.1093/bioinformatics/bth141
URL : https://hal.archives-ouvertes.fr/hal-00433587
Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002. ,
Learning important features through propagating activation differences, International Conference on Machine Learning (ICML), pp.3145-3153, 2017. ,
Reverse-complement parameter sharing improves deep learning models for genomics. bioRxiv, 2017. ,
DOI : 10.1101/103663
URL : https://www.biorxiv.org/content/biorxiv/early/2017/01/27/103663.full.pdf
Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,
Why transcription factor binding sites are ten nucleotides long, Genetics, vol.192, issue.3, pp.973-985, 2012. ,
Using the nyström method to speed up kernel machines, Advances in Neural Information Processing Systems (NIPS), pp.682-688, 2001. ,
Convolutional neural network architectures for predicting DNA-protein binding, Bioinformatics, vol.32, issue.12, pp.121-127, 2016. ,
DOI : 10.1093/bioinformatics/btw255
URL : https://doi.org/10.1093/bioinformatics/btw255
Predicting effects of noncoding variants with deep learning-based sequence model, Nature Methods, vol.12, issue.10, pp.931-934, 2015. ,
DOI : 10.1038/nmeth.3547
URL : http://europepmc.org/articles/pmc4768299?pdf=render
Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software (TOMS), vol.23, issue.4, pp.550-560, 1997. ,
DOI : 10.1145/279232.279236