Dear Mr Li,

Thank you very much for submitting your manuscript "SCMFMDA: Predicting microRNA-disease Associations based on Similarity Constrained Matrix Factorization" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

Please carefully revise your paper according to the reviews.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

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Please carefully revise your paper according to the reviews.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: In this study, Li et al. proposed a computational model to predict miRNA-disease associations based on similarity constrained matrix factorization. Their method, SCMFMDA, achieved better performance than existing ones and accurately predict relations between miRNAs and diseases in the following case studies. The model seems valid and the results they show are promising. However, following concerns should be addressed before considering for publication.

- There are three major issues for model validation: First, the authors should provide more details about the dataset they used for validation. They only mentioned: “Based on the verified association of miRNA-disease in HMDD V2.0 database”, which is not enough for the readers to fully understand the dataset. For examples, how many verified associations included in the dataset? How many unknown cases in the dataset? Do they perform any filtering to the dataset? Second, the authors should summarize the existing methods they compete and show the information including the publication time, the mathematic model they use, the dataset they used for training etc. in a table. The authors should also provide the details about how they compare these methods. To my understanding, it’s better to compare different methods in an independent testing set instead of on their own training set. Different methods can be trained on different datasets, it’s unclear how the authors compare these methods using cross validation. Third, the authors should provide the dataset they use as well as the source codes to execute their and others’ models, so that others can reproduce their results to confirm the correctness.

- As for parameter optimization, I’m not sure why they first fix two to optimize the third and then fix the third to optimize other two. And they don’t mention the exact values of two parameters they use to optimize the third. Just randomly pick some values? A more straightforward way is to test all the 490 possible combinations of three parameters and select the combination with best performance. Is this strategy very time-consuming? Figure 5a is not very professional, the dots representing the exact values should be plotted on the figure with a more smoothed curve to show the trend. The first two values on the figure is weird, why there is no value for 0 but two values for 10%? A color bar should be added for the heatmap in figure 5b. They grey line surround the heatmap can be removed.

- For case studies, a more detailed statistics of the performance can be shown in addition to three tables. For examples, for each disease, how many percentages of miRNAs in the top 50 can find evidences in HMDD v3.2 or dbDEMC v2.0 or both? How many cannot find clues in the database. Also, what’ s the differences between HMDD v3.2 and v2.0, which is used for model training? Are there any overlaps? if so, the authors should exclude those involved in the model training.

- The organization of the “Results” part is not quite reasonable. Logically, the parameter optimization should be in front of model comparison. Model comparison can be separated to non-ML methods and ML methods, but I would suggest putting them together. The last part can be the case studies to further confirm the prediction value of the method. In this way, the reader can better follow the logic of the results.

Reviewer #2: This paper proposed a new approach for miRNA-disease associations prediction. Experimental results indicated that this approach can effectively and efficiently predict miRNA-disease associations. This manuscript is well-written, which makes it easy to understand, although there are some minor issues to be addressed. I have the following specific comments.

1. There are some typos and grammatical errors throughout the paper, the authors should recheck the whole paper carefully to revise these problems.

2. There is an error in Equation (26) and Equation (32) in section of METHODS (D. Optimization algorithm). The A(:,j) should be changed to 〖A(:,j)〗^T. The authors should revise this error..

3. The authors ranked the miRNAs associated with diseases in section of RESULTS (C. Case studies). In my opinion, the authors should specifically introduce how to get the ranking of these miRNAs.

4. Authors compared this model with other MF-based models in section of RESULTS (D. Comparison with MF-based Models). Many matrix factorization-based models have developed for miRNA-disease prediction in a recent time. I think you should compare your model with them.

5. In your paper, the “Gaussian interaction profile kernel similarity” are referred to as “GIP kernel similarity”, but you used “GIP similarity” to referred in a few places in your paper. The authors should pay attention and make changes.

6. Many important computational models for miRNA-disease association prediction published in the top journals such as Bioinformatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics should be discussed and cited. This research field has made much progress in recent several years. Author should mention more recent computational studies for example:

Y. Zhao, X. Chen, and J. Yin, “Adaptive boosting-based computational model for predicting potential miRNA-disease associations,” Bioinformatics, vol. 35, no. 22, pp. 4730-4738, Apr. 2019.

C. Ji, Y. Wang, Z. Gao and L. Li, “A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder,” vol.1, no. 1, p.99, Mar. 2021.

Reviewer #3: In this paper, the authors use similarity network fusion and similarity constrained matrix factorization for miRNA-disease association prediction (SCMFMDA), which completes the missing associated scores between miRNAs and diseases. According to the relevant results, it demonstrates that SCMFMDA is effective for prediction of possible associations of miRNA-disease. However, there are existing some detailed problems.

1. Authors should revise your English writing carefully and eliminate small errors in the paper to make the paper easier to understand.

2. Authors should explain in details the function and impact of parameter ε in Equation (24).

3. The AUC values of global LOOCV of SCMFMDA, MSCHLMDA, GRL-NMF, IMCMDA, ICFMDA and SACMDA were introduced in the section of RESULTS (A. Model validation), but the corresponding values in Fig. 3 look different.

4. There is a reference error in Table III. Authors use ‘a’ and ‘b’ to denote ‘HMDD v3.2 database’ and ‘dbDEMC v2.0 database’ in Table III, respectively, but label ‘H’ and ‘d’ to denote ‘HMDD v3.2 database’ and ‘dbDEMC v2.0 database’ under the Table III.

5. Authors should pay attention to formatting errors which should be further adjusted. For example, there are some fonts are so small in Fig. 2.

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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 #1: No: I don't see the link to the source code and dataset they used to train and compare their model with others'

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure Files:

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. 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 us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your 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 Mr Li,

Thank you very much for submitting your manuscript "SCMFMDA: Predicting microRNA-disease Associations based on Similarity Constrained Matrix Factorization" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please revise quickly

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Please revise quickly

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have made substantial efforts to address the reviewers' concerns and the revised manuscript improved from many aspects. I have one more question, in the data availability section, the authors claimed "All relevant data are within the manuscript and its Supporting Information files" , yet I haven't found the code and datasets they used in their supporting information files or any link to public website for data sharing in their manuscript. Do I miss it or they don't provide it?

Reviewer #2: All my concerns have been solved.

Reviewer #3: The question of the manuscript has been answered correctly. The paper is acceptable.

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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 #1: No: I haven't found the code and datasets they used in their supporting information files or any link to public website for data sharing in their manuscript.

Reviewer #2: Yes

Reviewer #3: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure Files:

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. 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 us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your 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

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Mr Li,

We are pleased to inform you that your manuscript 'SCMFMDA: Predicting microRNA-disease Associations based on Similarity Constrained Matrix Factorization' 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.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

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