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CORRECTION article

Front. Genet., 24 October 2022
Sec. Computational Genomics
Volume 13 - 2022 | https://doi.org/10.3389/fgene.2022.1062928

Corrigendum: Research on the mechanism of soybean resistance to Phytophthora infection using machine learning Methods

This article is a correction to:

  1. Research on the Mechanism of Soybean Resistance to Phytophthora Infection Using Machine Learning Methods

    1. Read original article
Junxia Chi,Junxia Chi1,2Shizeng Song,Shizeng Song2,3Hao Zhang,,Hao Zhang1,2,3Yuanning Liu,,Yuanning Liu1,2,3Hengyi Zhao,Hengyi Zhao2,3Liyan Dong,, Liyan Dong1,2,3*
  • 1College of Software, Jilin University, Changchun, China
  • 2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
  • 3College of Computer Science and Technology, Jilin University, Changchun, China

A Corrigendum on
Research on the mechanism of soybean resistance to Phytophthora infection using machine learning Methods

by Chi J, Song S, Zhang H, Liu Y, Zhao H and Dong L (2021). Front. Genet. 12:634635. doi: 10.3389/fgene.2021.634635

In the published article, the affiliation of “College of Software, Jilin University, Changchun, China” was erroneously omitted and the authors’ affiliations had incorrect attributions as a result.

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.

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.

Keywords: sRNA data analysis, differential expression, machine learning, resistance mechanism, Phytophthora sojae

Citation: Chi J, Song S, Zhang H, Liu Y, Zhao H and Dong L (2022) Corrigendum: Research on the mechanism of soybean resistance to Phytophthora infection using machine learning Methods. Front. Genet. 13:1062928. doi: 10.3389/fgene.2022.1062928

Received: 06 October 2022; Accepted: 10 October 2022;
Published: 24 October 2022.

Copyright © 2022 Chi, Song, Zhang, Liu, Zhao and Dong. 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: Liyan Dong, ZG9uZ2x5QGpsdS5lZHUuY24=

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.