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We show that deep learning models, and especially architectures like the Transformer, originally intended for natural language, can be trained on randomly generated datasets to predict to very high accuracy both the qualitative and quantitative features of metabolic networks.
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Jeremy Gunawardena · 2012
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Laplacian dynamics on general graphs
Inomzhon Mirzaev and Jeremy Gunawardena · 2013
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Neural machine translation by jointly learning to align and translate
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Bernhard Palsson · 2015
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Quantitative systems pharmacology: a promising approach for translational pharmacology
K Gadkar, D Kirouac, N Parrott, and S Ramanujan · 2016
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Applied concepts in pbpk modeling: How to build a pbpk/pd model
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Unsupervised machine translation using monolingual corpora only
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Attention is all you need
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History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications
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