Compound RNN to predict MICs using K-Mer Fingerprints and Antibiotic SMILES

Author:

Kromer-Edwards Cory1ORCID,Oliveira Suely2ORCID

Affiliation:

1. Machine Translation, Ontario, Ohio, USA

2. Computer Science, University of Iowa, Iowa City, Iowa, USA

Publisher

ACM

Reference26 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

2. E. Anderson G. D. Veith and David Weininger. 1987. SMILES: a line notation and computerized interpreter for chemical structures. (1987). E. Anderson G. D. Veith and David Weininger. 1987. SMILES: a line notation and computerized interpreter for chemical structures. (1987).

3. Randomized SMILES Strings Improve the Quality of Molecular Generative Models

4. Neural network-based predictions of antimicrobial resistance in Salmonella spp. using k-mers counting from whole-genome sequences

5. Antimicrobial resistance in the next 30 years, humankind, bugs and drugs: a visionary approach

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