In the published article, there was an error in affiliation(s) 1. Instead of “Faculty of Information Technology, Macau University of Science and Technology, Taipa, China,” it should be “Faculty of Information Technology, Macau University of Science and Technology, Macau, China.”
In the published article, there was an error. Mathematical symbols are inconsistent.
A correction has been made to 3 Methods, “3.1 CP decomposition,” Paragraph Number 5.
This sentence previously stated:
“CANDECOMP/PARAFAC (CP) decomposition is one of the most common tensor decomposition forms (Kolda and Bader, 2009). Given the miRNA-disease-type tensor , the CP decomposition model can be represented as follows:where the symbol ◦ represents the vector outer product, S is a positive integer and , and . M = [m1m2 ⋯ mS], D = [d1d2 ⋯ dS], and T = [t1t2 ⋯ tS] are the factor matrices with respect to different dimensions.”
The corrected sentence appears below:
“CANDECOMP/PARAFAC (CP) decomposition is one of the most common tensor decomposition forms (Kolda and Bader, 2009). Given the miRNA-disease-type tensor , the CP decomposition model can be represented as follows:where the symbol ◦ represents the vector outer product, S is a positive integer and , and . M = [m1m2 ⋯ mS], D = [d1d2 ⋯ dS], and T = [t1t2 ⋯ tS] are the factor matrices with respect to different dimensions.”
Note that mathematic symbols are bolded to represent vectors. Also, “, and ” should be changed to “, and .”
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.
Statements
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Reference
1
KoldaT. G.BaderB. W. (2009). Tensor decompositions and applications. SIAM Rev.51, 455–500. 10.1137/07070111X
Summary
Keywords
multiple types of miRNA–disease associations, weighted tensor decomposition, graph Laplacian regularization, L2, 1 norm, multi-view biological similarity network
Citation
Ouyang D, Miao R, Wang J, Liu X, Xie S, Ai N, Dang Q and Liang Y (2022) Corrigendum: Predicting multiple types of associations between miRNAs and diseases based on graph regularized weighted tensor decomposition. Front. Bioeng. Biotechnol. 10:1006237. doi: 10.3389/fbioe.2022.1006237
Received
29 July 2022
Accepted
08 August 2022
Published
30 August 2022
Volume
10 - 2022
Edited and reviewed by
Qi Zhao, University of Science and Technology Liaoning, China
Updates
Copyright
© 2022 Ouyang, Miao, Wang, Liu, Xie, Ai, Dang and Liang.
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: Yong Liang, yongliangresearch@gmail.com
This article was submitted to Bioprocess Engineering, a section of the journal Frontiers in Bioengineering and Biotechnology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.