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Each human genome is a 3 billion base pair set of encoding instructions.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Transcriptional regulatory elements in the human genome
Glenn A. Maston, Sarah K. Evans, and Michael R. Green · 2006
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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An integrated encyclopedia of dna elements in the human genome
ENCODE Project Consortium · 2012
Earlier work this paper cites.
Predicting cell-type–specific gene expression from regions of open chromatin
Anirudh Natarajan, Galip Gürkan Yardımcı, Nathan C. Sheffield, Gregory E. Crawford, and Uwe Ohler · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Identification of transcriptional regulators in the mouse immune system
Vladimir Jojic, Tal Shay, Katelyn Sylvia, Or Zuk, Xin Sun, Joonsoo Kang, Aviv Regev, Daphne Koller, Immunological Genome Project Consortium, et al · 2013
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
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Training very deep networks
Rupesh K Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks
David R Kelley, Jasper Snoek, and John Rinn · 2015
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Jaspar 2016: a major expansion and update of the open-access database of transcription factor binding profiles
Anthony Mathelier, Oriol Fornes, David J Arenillas, Chih-yu Chen, Grégoire Denay, Jessica Lee, Wenqiang Shi, Casper Shyr, Ge Tan, Rebecca Worsley-Hunt, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Benjamin Graham · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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URL http://epd.vital-it.ch/mouse/mouse_database.php
Eukaryotic promoter database
Cited in the paper.
Immgen microarray gene expression data: Data generation and quality control pipeline
Jeff Ericson, Scott Davis, Jon Lesh, Melissa Howard, Diane Mathis, and Christophe Benoist
Cited in the paper.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep motif: Visualizing genomic sequence classifications
Jack Lanchantin, Ritambhara Singh, Zeming Lin, and Yanjun Qi · 2016
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Deep residual networks with exponential linear unit
Anish Shah, Eashan Kadam, Hena Shah, and Sameer Shinde · 2016
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