PCOMPBIOL-D-24-01942

NeKo: a tool for automatic network construction from prior knowledge

PLOS Computational Biology

Dear Dr. Ruscone,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

​Please submit your revised manuscript within 60 days Mar 08 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Alberto J M Martin, Ph.D.

Academic Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

Additional Editor Comments:

Please do address issues raised by reviewers, specially those regarding escalation on larger datasets, limitations and validation of the tool

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Please see the attachment in the online system.

Reviewer #2: The manuscript "NeKo: a tool for automatic network construction from prior knowledge" provides valuable insights into biological networks in the filed of transcriptomics. The authors have done excellent work in developing a Python package for construction of biological networks by integrating molecular interactions from various databases. The study aims to develop a Python package for construction of molecular network and also model drug synergies. The tool is available in github. However, the following issues need to be addressed before publication.

1) Which databases are integrated for genes and drugs to be addressed more specifically.

2) Comparison with similar packages and its advances to be highlighted.

3) Limitations of NeKo are to listed.

Reviewer #3: Review of “NeKo: a tool for automatic network construction from prior knowledge”

The manuscript introduces NeKo, a Python-based tool designed to automate the construction of biological networks by integrating molecular interactions from multiple prior knowledge databases. The authors aim to reduce the time-consuming and labor-intensive manual curation traditionally required in knowledge-driven network construction while providing flexibility. The manuscript highlights two use cases: constructing a network for medulloblastoma subgroup-specific signatures and modeling drug synergies using logical models.

NeKo represents a significant step forward in streamlining network construction and demonstrates the potential for broad application in systems biology. However, several major and minor issues need to be addressed to enhance the manuscript's clarity, rigor, and utility.

Major Comments

1. The authors provide a solid section on strategy selection, including descriptions of available algorithms and their applications. However, as strategy selection is a key challenge for usability, this section could be broadened to include more detailed guidance, examples, or decision trees to assist users in selecting the optimal strategy for their specific research questions. Clearer recommendations would improve accessibility for non-expert users and reduce the learning curve.

2. NeKo’s model for predicting drug synergies performed relatively poorly compared to the manually curated model. The manuscript should discuss whether the chosen strategy was optimal for this task and provide insights into how strategy selection influences performance. Was the poor result due to inherent limitations in the method or suboptimal strategy alignment?

3. While the manuscript discusses NeKo’s integration with databases like OmniPath and SIGNOR, a systematic evaluation of how database choice impacts network quality is important. This is particularly substantial given NeKo's reliance on external resources, and the lack of such benchmarking may limit confidence in its results.

4. While NeKo is tested on small-to-medium networks, its performance on larger datasets (>1000 nodes) is not discussed. Computational benchmarks assessing runtime and memory usage across varying network sizes would be helpful, especially for researchers handling large omics datasets.

5. Comparing NeKo’s functionality and outputs with widely used alternatives, such as STRING or Ingenuity Pathway Analysis (IPA), would provide context for its capabilities. Highlighting NeKo's strengths (e.g., flexibility, export formats) and limitations relative to these tools would better position it within the field of network construction.

Minor Comments

- The manually curated network's AUC value is not reported in Figure 5, making it difficult to assess the performance gap between NeKo-derived and curated networks. This metric should be included for a complete comparison.

- On page 4, the text mentions "activation" edges, while Figure 2 refers to "stimulation."

- On page 9, a citation appears as "(REF)." This needs correction.

- On page 10, "Addittional" contains a typographical error.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Dr. Dicky John Davis G

Reviewer #3: Yes: Ábel Fóthi

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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Attachments

Attachment

Submitted filename: Review.docx

PCOMPBIOL-D-24-01942R1

NeKo: a tool for automatic network construction from prior knowledge

PLOS Computational Biology

Dear Dr. Ruscone,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

​Please submit your revised manuscript within 60 days Jul 01 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Alberto J M Martin, Ph.D.

Academic Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

Journal Requirements:

Please amend your detailed Financial Disclosure statement. This is published with the article. It must therefore be completed in full sentences and contain the exact wording you wish to be published.Please ensure that the funders and grant numbers match between the Financial Disclosure field and the Funding Information tab in your submission form. Note that the funders must be provided in the same order in both places as well.

1) State the initials, alongside each funding source, of each author to receive each grant. For example: "This work was supported by the National Institutes of Health (####### to AM; ###### to CJ) and the National Science Foundation (###### to AM)."

2) State what role the funders took in the study. If the funders had no role in your study, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.".

If you did not receive any funding for this study, please simply state: u201cThe authors received no specific funding for this work.u201d

Reviewers' comments:

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Ruscone,

We are pleased to inform you that your manuscript 'NeKo: a tool for automatic network construction from prior knowledge' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Mark Alber, Ph.D.

Section Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #3: Thank you for your answer.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: None

**********

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Reviewer #3: Yes: Ábel Fóthi