NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations
- 1School of Life Sciences, Northwestern Polytechnical University, Xi'an, China
- 2Department of Computer Science, The University of Hong Kong, Hong Kong, China
- 3School of Computer Science, Northwestern Polytechnical University, Xi'an, China
A Corrigendum on
NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations
by Zhu, B., Xu, Y., Zhao, P., Yiu, S.-M., Yu, H., and Shi, J.-Y. (2022). Front. Microbiol. 13:846915. doi: 10.3389/fmicb.2022.846915
In the original article, there was an error in “affiliation 1” as published. Instead of “School of Life Sciences, Northwestern Polytechnic University, Xi'an, China,” it should be “School of Life Sciences, Northwestern Polytechnical University, Xi'an, China.”
In the original article, there was an error in “affiliation 3” as published. Instead of “School of Computer Science, Northwestern Polytechnic University, Xi'an, China,” it should be “School of Computer Science, Northwestern Polytechnical University, Xi'an, China.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Keywords: deep learning, bipartite graph network, link prediction, drug-microbe association, attention matrix
Citation: Zhu B, Xu Y, Zhao P, Yiu S-M, Yu H and Shi J-Y (2022) Corrigendum: NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations. Front. Microbiol. 13:944952. doi: 10.3389/fmicb.2022.944952
Received: 16 May 2022; Accepted: 17 May 2022;
Published: 30 May 2022.
Copyright © 2022 Zhu, Xu, Zhao, Yiu, Yu and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hui Yu, aHVpeXUmI3gwMDA0MDtud3B1LmVkdS5jbg==; Jian-Yu Shi, amlhbnl1c2hpJiN4MDAwNDA7bndwdS5lZHUuY24=
†These authors have contributed equally to this work and share first authorship