Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nature Biotechnology, vol.13, issue.8, pp.831-838, 2015. ,
DOI : 10.1126/science.1162327
Deep learning for computational biology, Molecular Systems Biology, vol.12, issue.7, p.878, 2016. ,
DOI : 10.15252/msb.20156651
URL : http://msb.embopress.org/content/msb/12/7/878.full.pdf
The PRINTS protein fingerprint database in its fifth year, Nucleic Acids Research, vol.26, issue.1, pp.304-308, 1998. ,
DOI : 10.1093/nar/26.1.304
URL : https://academic.oup.com/nar/article-pdf/26/1/304/7048381/26-1-304.pdf
DREME: motif discovery in transcription factor ChIP-seq data, Bioinformatics, vol.27, issue.12, pp.1653-1659, 2011. ,
DOI : 10.1093/bioinformatics/btr261
URL : https://academic.oup.com/bioinformatics/article-pdf/27/12/1653/17124808/btr261.pdf
Hidden Markov models of biological primary sequence information., Proceedings of the National Academy of Sciences, pp.1059-1063, 1994. ,
DOI : 10.1073/pnas.91.3.1059
URL : http://www.pnas.org/content/91/3/1059.full.pdf
Support Vector Machines and Kernels for Computational Biology, PLoS Computational Biology, vol.14, issue.10, 2008. ,
DOI : 10.1371/journal.pcbi.1000173.t002
URL : https://doi.org/10.1371/journal.pcbi.1000173
Group invariance and stability to deformations of deep convolutional representations . arXiv preprint, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01536004
Multitask Learning: A Knowledge-Based Source of Inductive Bias, International Conference on Machine Learning (ICML), 1993. ,
DOI : 10.1016/B978-1-55860-307-3.50012-5
Can we open the black box of AI?, Nature, vol.538, issue.7623, pp.20-23, 2016. ,
DOI : 10.1038/538020a
A unified architecture for natural language processing, Proceedings of the 25th international conference on Machine learning, ICML '08, 2008. ,
DOI : 10.1145/1390156.1390177
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons, BMC Genomics, vol.6, issue.3, p.754, 2016. ,
DOI : 10.1111/1574-6976.12036
URL : https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-016-2889-6?site=bmcgenomics.biomedcentral.com
Liblinear: A library for large linear classification, Journal of Machine Learning Research (JMLR), vol.9, pp.1871-1874, 2008. ,
Comparing biases for minimal network construction with backpropagation, Advances in Neural Information Processing Systems (NIPS), pp.177-185, 1989. ,
A Discriminative Framework for Detecting Remote Protein Homologies, Journal of Computational Biology, vol.7, issue.1-2, pp.95-114, 2000. ,
DOI : 10.1089/10665270050081405
URL : http://galileo.gmu.edu/~lhunter/reprints/haussler.ps
Integrative deep models for alternative splicing, Bioinformatics, vol.33, issue.14, pp.274-282, 2017. ,
DOI : 10.1093/bioinformatics/btx268
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks, Genome Research, vol.26, issue.7, pp.990-999, 2016. ,
DOI : 10.1101/gr.200535.115
Adam: A method for stochastic optimization. arXiv preprint, 2014. ,
Denoising genome-wide histone ChIP-seq with convolutional neural networks, Bioinformatics, vol.33, issue.14, pp.225-233, 2017. ,
DOI : 10.1093/bioinformatics/btx243
Hidden Markov Models in Computational Biology, Journal of Molecular Biology, vol.235, issue.5, pp.1501-1531, 1994. ,
DOI : 10.1006/jmbi.1994.1104
A simple weight decay can improve generalization, Advances in Neural Information Processing Systems (NIPS), pp.950-957, 1992. ,
DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS, Biocomputing 2017, 2016. ,
DOI : 10.1142/9789813207813_0025
Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015. ,
DOI : 10.1007/s10994-013-5335-x
Unsupervised feature learning for audio classification using convolutional deep belief networks, Advances in Neural Information Processing Systems (NIPS), 2009. ,
DOI : 10.1145/2001269.2001295
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
On the limited memory BFGS method for large scale optimization, Mathematical Programming, pp.503-528, 1989. ,
DOI : 10.1007/BF01589116
URL : http://www.ece.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf
JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles, Nucleic Acids Research, vol.44, issue.D1, pp.44-110, 2016. ,
DOI : 10.1093/nar/gkv1176
URL : https://hal.archives-ouvertes.fr/hal-01281181
Convolutional kitchen sinks for transcription factor binding site prediction. arXiv preprint, 2017. ,
Generalized Max Pooling, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. ,
DOI : 10.1109/CVPR.2014.317
URL : http://arxiv.org/pdf/1406.0312
The Spatiotemporal Program of DNA Replication Is Associated with Specific Combinations of Chromatin Marks in Human Cells, PLoS Genetics, vol.15, issue.5, p.1004282, 2014. ,
DOI : 10.1371/journal.pgen.1004282.s014
URL : https://hal.archives-ouvertes.fr/hal-00995097
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
A Generalized Representer Theorem, Computational Learning Theory, 2001. ,
DOI : 10.1007/3-540-44581-1_27
Learning with kernels: support vector machines, regularization, optimization , and beyond, 2002. ,
Kernel methods for pattern analysis, 2004. ,
DOI : 10.1017/CBO9780511809682
Learning important features through propagating activation differences, International Conference on Machine Learning (ICML) ,
Reverse-complement parameter sharing improves deep learning models for genomics. bioRxiv, p.103663, 2017. ,
DOI : 10.1101/103663
URL : http://biorxiv.org/content/biorxiv/early/2017/01/27/103663.full.pdf
Discovery of novel transcription factor binding sites by statistical overrepresentation, Nucleic Acids Research, vol.30, issue.24, pp.5549-5560, 2002. ,
DOI : 10.1093/nar/gkf669
URL : https://academic.oup.com/nar/article-pdf/30/24/5549/3751278/gkf669.pdf
Sparse greedy matrix approximation for machine learning, International Conference on Machine Learning (ICML), 2000. ,
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. ,
DOI : 10.1534/genetics.112.143370
URL : http://www.genetics.org/content/genetics/192/3/973.full.pdf
DNA binding sites: representation and discovery, Bioinformatics, vol.16, issue.1, pp.16-23, 2000. ,
DOI : 10.1093/bioinformatics/16.1.16
URL : https://academic.oup.com/bioinformatics/article-pdf/16/1/16/669871/160016.pdf
org: a wiki-based database for transcription factor-binding data generated by the encode consortium, Nucleic Acids Research, issue.D1, pp.41-171, 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.32-121, 2016. ,
DOI : 10.1093/bioinformatics/btw255
URL : http://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 : 10.1371/journal.pcbi.1001025
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768299/pdf
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software, vol.23, issue.4, pp.550-560, 1997. ,
DOI : 10.1145/279232.279236