Figure 1 legend
In the original article, there were mistakes in the legend for Figure 1 as published.
FIGURE 1
Mistakes which were made:
1) Lowercase letter after a full-stop: “…causal structures. meta-learning” instead of “…causal structures. Meta-learning”
2) “meta-moelling” instead of “meta-modelling”
3) “simplicitly” instead of “simplicity.”
The original article has been updated
Figure 2 legend
In the original article, there was a mistake in the legend for Figure 2 as published. Mistakes which was made: the sentence “Outline of a computational procedure using upstream modular meta-modelling for the inference of causal structures.” has to be removed since it is has been copied by mistake from the caption of Figure 1. The correct legend appears below.
FIGURE 2
The original article has been updated
Text Correction
In the original article, there were some errors.
1) Mistake that was made: verb in the infinitive instead of in the gerund mode, specifically "represent" instead of "representing".
A correction has been made to Section 2, Sub-section 2.1 on page 5 in the middle of the second column, as follows:
“Both are used to define the output and input relationships and then may be used to identify the best model representing the behaviour of the data (Hartmann et al., 2019).”
2) Mistake that was made: name in the plural instead of the singular, specifically “problems” instead of “problem”.
A correction has been made to Section 2, Sub-section 2.2 on page 8 at the beginning of the first column. The correct sentence is
“It is however well known that biological network inference is in many realistic situations an undetermined problem, since the size of the node covariate sample is small, whereas the size of the network is huge.”
3) Mistake that was made: repeated sentence
A correction has been made to Section 2, at the end of Sub-section 2.2. One instance of the sentence below was removed.
“For example, using the difference between the expression levels of genes in a gene network or protein concentrations in a protein-protein network might be insufficient for the purposes of causal inference, since cause-effect relations between nodes might not manifest themselves through a variation of this distance and might not manifest themselves only through appropriate behaviour of this distance.”
4) Mistake that was made: typos and singular/plural use errors. “ad G-computations” instead “as G-computation”; “This findings” instead of “These findings”; “…in this sections … ” instead of “…in this section … ”
A correction has been made to Section 2, at the end of Sub-section 2.4. The correct sentence appears below.
“Finally, a recent work by Le Borgne et al. (2021) not specifically on biological networks, but on treatment-effect networks, found that SVM approach is competing with the most powerful recent methods, such as G-computation (Snowden et al., 2011) for small sample sizes with one hundred nodes when the relationships between the covariates and the outcome are complex. These findings, as well as the literature mentioned in this section, constitute important insights into the development of an efficient future causal version of SVMs.”
5) Mistakes that were made: singular instead of a plural. i.e., “challenge” instead of “challenges,” and a full-stop instead of “:”
A correction has been made to Section 3, in the first sentence. The correct sentence is
“The two main challenges that machine learning algorithms have to face are: …”
The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way.
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.
Summary
Keywords
machine learning, deep learning, causality, inference, causal thinking, artificial intelligence, systems biology
Citation
Lecca P (2022) Corrigendum: Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. Front. Bioinform. 2:888273. doi: 10.3389/fbinf.2022.888273
Received
02 March 2022
Accepted
03 March 2022
Published
06 April 2022
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
2 - 2022
Updates
Copyright
© 2022 Lecca.
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: Paola Lecca, Paola.Lecca@unibz.it
This article was submitted to Network Bioinformatics, a section of the journal Frontiers in Bioinformatics
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.