In the original article, there was an error. The “ExInAtor” code was incorrectly used for a comparison with our method. ExInAtor is designed to only be used with mutations from Whole Genome Sequences, not with Whole Exome Sequences as was done in this article. As such, the ExInAtor results present in the Supplementary Material and those referenced in the text and Figures should be discounted.
A correction has been made to Figure 4. The corrected Figure 4 appears below.
Figure 4
Further, a correction has been made to Figure 5. The corrected Figure 5 appears below.
Figure 5
Lastly, a correction has been made to Table 1. The corrected Table 1 appears below.
Table 1
| Methods | CGC overlap | NCG overlap | Consensus No.1 | Consensus No.2 | CGC rank | NCG rank | Consensus No.1 rank | Consensus No.2 rank | Average rank |
|---|---|---|---|---|---|---|---|---|---|
| ActiveDriver | 17.92% | 38.51% | 52.28% | 2.03% | 17 | 17 | 18 | 20 | 18 |
| Dendrix | 28.75% | 42.26% | 69.38% | 19.11% | 12 | 15 | 12 | 6 | 11.25 |
| MDPFinder | 28.82% | 51.58% | 79.34% | 24.15% | 11 | 6 | 8 | 2 | 6.75 |
| Simon | 29.25% | 45.26% | 62.13% | 7.36% | 9 | 11 | 14 | 14 | 12.25 |
| NetBox | 26.41% | 54.26% | 74.18% | 11.10% | 15 | 4 | 11 | 13 | 10.75 |
| OncoDriveFM | 26.52% | 42.04% | 76.92% | 13.96% | 14 | 16 | 10 | 9 | 12.25 |
| MutSigCV | 37.07% | 51.30% | 89.94% | 18.24% | 5 | 7 | 3 | 8 | 5.75 |
| MEMo | 17.07% | 18.17% | 18.71% | 11.37% | 18 | 22 | 23 | 11 | 18.5 |
| CoMDP | 6.70% | 20.39% | 37.89% | 0.51% | 22 | 21 | 19 | 22 | 21 |
| DawnRank | 31.66% | 44.97% | 36.60% | 3.08% | 8 | 12 | 20 | 18 | 14.5 |
| DriverNet | 39.38% | 50.67% | 59.15% | 22.39% | 3 | 9 | 16 | 3 | 7.75 |
| e-Driver | 36.07% | 51.05% | 78.85% | 28.65% | 6 | 8 | 9 | 1 | 6 |
| iPAC | 11.13% | 29.16% | 32.71% | 1.38% | 21 | 20 | 21 | 21 | 20.75 |
| MSEA | 13.36% | 32.01% | 64.81% | 2.58% | 20 | 19 | 14 | 19 | 18 |
| OncoDriveCLUST | 44.32% | 21.61% | 87.10% | 19.38% | 13 | 13 | 6 | 7 | 9.75 |
| DrGap | 18.81% | 42.69% | 88.79% | 3.30% | 16 | 14 | 4 | 16 | 12.5 |
| DriverML | 48.19% | 70.55% | 94.01% | 20.38% | 2 | 2 | 2 | 5 | 2.75 |
| OncodriveFML | 33.78% | 48.03% | 81.15% | 11.02% | 7 | 10 | 7 | 12 | 9 |
| SCS | 5.15% | 1.32% | 19.66% | 0.23% | 23 | 23 | 22 | 23 | 22.75 |
| rDriver | 38.18% | 53.17% | 87.89% | 12.97% | 4 | 5 | 5 | 10 | 6 |
| UniCovEx | 29.01% | 55.70% | 65.56% | 3.52% | 10 | 3 | 13 | 17 | 10.75 |
| FI-net | 53.01% | 88.20% | 95.18% | 21.46% | 1 | 1 | 1 | 4 | 1.75 |
Overall performances of 22 driver gene prediction methods on 31 TCGA datasets.
The bold number indicates the best 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.
Summary
Keywords
cancer research, driver genes, functional impact, artificial neural network, multi-omics features, hierarchical clustering algorithm
Citation
Gu H, Xu X, Qin P and Wang J (2021) Corrigendum: FI-Net: Identification of Cancer Driver Genes by Using Functional Impact Prediction Neural Network. Front. Genet. 12:675351. doi: 10.3389/fgene.2021.675351
Received
03 March 2021
Accepted
15 March 2021
Published
13 April 2021
Volume
12 - 2021
Edited and reviewed by
Yunyan Gu, Harbin Medical University, China
Updates
Copyright
© 2021 Gu, Xu, Qin and Wang.
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: Pan Qin qp112cn@dlut.edu.cnJia Wang wangjia77@hotmail.com
This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
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