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De novo genome assembly focuses on finding connections between a vast amount of short sequences in order to reconstruct the original genome.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
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Pointer networks
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al · 2016
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Neural message passing for quantum chemistry
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Relational inductive bias for physical construction in humans and machines
Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
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Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2019
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Dobrik Georgiev and Pietro Lió · 2020
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Xlvin: executed latent value iteration nets
Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, and Mladen Nikolić · 2020
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Raven: a de novo genome assembler for long reads
Robert Vaser and Mile Sikic · 2020
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Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
Cited alongside, same era.