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Research article
Qiu Xiao
Hunan Normal University
Corresponding Author
Jiahong Yang
Hunan Normal University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Some proposed methods for identifying essential proteins have better results by using
biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.
Results: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.
Conclusions: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.
Keywords
essential proteins, the PPI network, Jaccard similarity index, Edge clustering coefficient
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 2
Posted 14 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 01 Mar, 2021
Review #2 received
Received 26 Feb, 2021
Reviewer #2 agreed
On 27 Jan, 2021
Reviewers invited
Invitations sent on 24 Jan, 2021
Reviewer #1 agreed
On 24 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Editor assigned
On 02 Dec, 2020
Submission checks complete
On 02 Dec, 2020
Editor invited
On 02 Dec, 2020
No comments provided
Editorial decision: Major revision
On 30 Sep, 2020
Review #1 received
Received 09 Sep, 2020
Review #2 received
Received 09 Sep, 2020
Reviewer #2 agreed
On 08 Sep, 2020
Reviewers invited
Invitations sent on 08 Sep, 2020
Reviewer #1 agreed
On 08 Sep, 2020
Editor assigned
On 09 Aug, 2020
Submission checks complete
On 08 Aug, 2020
Editor invited
On 08 Aug, 2020
First submitted
On 07 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Qiu Xiao
Hunan Normal University
Corresponding Author
Jiahong Yang
Hunan Normal University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 2
Posted 14 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 01 Mar, 2021
Review #2 received
Received 26 Feb, 2021
Reviewer #2 agreed
On 27 Jan, 2021
Reviewers invited
Invitations sent on 24 Jan, 2021
Reviewer #1 agreed
On 24 Jan, 2021
Review #1 received
Received 24 Jan, 2021
Editor assigned
On 02 Dec, 2020
Submission checks complete
On 02 Dec, 2020
Editor invited
On 02 Dec, 2020
No comments provided
Editorial decision: Major revision
On 30 Sep, 2020
Review #1 received
Received 09 Sep, 2020
Review #2 received
Received 09 Sep, 2020
Reviewer #2 agreed
On 08 Sep, 2020
Reviewers invited
Invitations sent on 08 Sep, 2020
Reviewer #1 agreed
On 08 Sep, 2020
Editor assigned
On 09 Aug, 2020
Submission checks complete
On 08 Aug, 2020
Editor invited
On 08 Aug, 2020
First submitted
On 07 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: Some proposed methods for identifying essential proteins have better results by using
biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.
Results: In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.
Conclusions: We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.
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Figure 3
Figure 4
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