This article is a correction to:
Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications
Bingran Shen
Gloria M. Coruzzi
Dennis Shasha1*- 1Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, United States
- 2Center for Genomics and Systems Biology, Department of Biology, New York University, New York, United States
A Corrigendum on
Bipartite networks represent causality better than simple networks: evidence, algorithms, and applications
by Shen, B., Coruzzi, G., and Shasha, D. (2024). Front. Genet. 15:1371607. doi: 10.3389/fgene.2024.1371607
In the published article, an Author name was incorrectly written as “Gloria Curozzi.” The corrected author name should read as “Gloria M. Coruzzi.”
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: RNA sequencing, gene regulatory network, causal inference, random forest, bipartite network
Citation: Shen B, Coruzzi GM and Shasha D (2024) Corrigendum: Bipartite networks represent causality better than simple networks: Evidence, algorithms, and applications. Front. Genet. 15:1440665. doi: 10.3389/fgene.2024.1440665
Received: 29 May 2024; Accepted: 31 May 2024;
Published: 18 June 2024.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2024 Shen, Coruzzi and Shasha. 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: Dennis Shasha, c2hhc2hhQGNpbXMubnl1LmVkdQ==