PONE-D-22-08273Predicting Adverse Drug Effects: A Heterogeneous Graph Convolution Network with a Multi-layer Perceptron ApproachPLOS ONE

Dear Dr. Shih,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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.

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We look forward to receiving your revised manuscript.

Kind regards,

Jinn-Moon Yang

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors proposed a graph convolution network (GCN) for predicting the drug adversary effects by inferring the hidden links between drugs and adverse effects. The results are compared with other non-machine learning methods for missing link prediction using the SIDER (side effect) database. Below please find the comments.

1. The prediction of drug side effects has been studied for decades. GCN, as well as other machine/deep learning approaches, have been applied in predicting side effects (e.g., see a few listed in [1-4]). The introduction is lacking of citing previous machine/deep learning works, which should be added in their introduction and the strength/weakness with the proposed network should be discussed.

[1] Modeling polypharmacy side effects with graph convolutional networks, Bioinformatics, 2018.

[2] Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models, Briefings in Bioinformatics, 2018.

[3] Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases, Frontiers in Drug Discovery, 2021.

[4] Predicting adverse drug reactions through interpretable deep learning framework, BMC Bioinformatics, 2018.

2. The training and validating datasets came solely from the SIDER database, while previous works often include more resources. For instance, in [1], in addition to SIDER, OFFSIDES and TWOSIDES were incorporated. In [2], BioRDF and FDA FAERS were used in the evaluation. The breadth and depth of the experimental results can be improved by following previous works.

3. The compared methods with GCN (e.g., common neighbors, Jaccard index) are not fair as they are non-ML and/or naïve methods which is easy to beat. I would suggest include a few previous ML/DL methods for link prediction (e.g., [1-4]) as these are more related, and this would be easier for the readers to understand the strength of the proposed network.

Reviewer #2: In this manuscript, the authors presented a heterogeneous Graph Convolution Network with a Multi-layer Perceptron (GCNMLP) approach to predict the adverse drug effects. The topic is essential, the prediction model is intriguing, and the results are promising. However, there are several major concerns that the authors need to address.

1. There are no references to similar studies of adverse drug effects prediction, and the related works must be included in the Introduction section.

2. The whole dataset should be described clearly. For example, how many drugs, side effects, positive and negative links between drugs and side effects are in the dataset?

3. The similarity score is the key to building the networks among drugs. Nevertheless, it is unclear how to apply the natural language processing method to calculate the similarity score between two drugs.

4. In Table 2, the authors compared the predictive performance by the different percentages of the training set. However, it is unknown the size of the testing set. It is better to use n-fold cross-validation to evaluate the performance appropriately.

5. The predictive performance was compared with several algorithms (Table 4), but it should be compared with other related works.

6. The discussion section should be extended and elaborated.

7. There are some grammar issues, and I would encourage the authors to have the manuscript proofread by a native English speaker to improve the grammar and word choice.

Reviewer #3: Some issues argued by the reviewer that need to be considered in this work, and I itemized below.

1. No doubt we agree it’s a very important issue in medicine. In addition to collect all recent works in the introduce, the authors should have to figure out the problems like the difficulty on further improvement or the limitation in various methods in the present for the readers. In the other hand, to avoid confusion and clarifying the pros and cons on the drug effects prediction in this manuscript, I suggest the authors use the same published dataset to make fair and objective comparison. It is hard to evaluate the contribution for this research.

2. The authors just descripted how they download the drug data and obtain the corresponding side effects but lacking the subsequent filtering processes or other management. Necessary information for a representative and convincing dataset is required. We did not even have no idea how many drugs used in this research.

3. It’s not clear on how to obtain the predictive performance. And the “2. A brief review of well-known algorithms” just listed the equations for each evaluation item without explain what they used for. And the authors have to do that.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PONE-D-22-08273R1Predicting Adverse Drug Effects: A Heterogeneous Graph Convolution Network with a Multi-layer Perceptron ApproachPLOS ONE

Dear Dr. Shih,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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 by Oct 06 2022 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 plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ 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 academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• 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, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jinn-Moon Yang

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed my comments by adding new results from OFFSIDERS and FAERS side effect databases.

Reviewer #2: The authors have revised the manuscript, but the point-by-point response are not provided. There are some comments of Reviewer #2 are not addressed.

Reviewer #4: This study proposed a heterogeneous graph convolution network with a multi-layer perceptron approach to predict adverse drug effects. The authors claimed that they tested their proposed method on three different datasets and achieved better performances than the non-negative matrix factorization method (NMF) and some well-known machine learning approaches. Furthermore, some new side effects of drugs can be identified by the proposed method. However, several issues should be addressed or clarified.

1. We strongly suggest the authors prepare an independent test set and report the performance of the proposed method based on it. Ten-fold cross-validation used in this study is for parameter optimization in machine learning. It could overestimate the ability of the proposed approach and result in an overfitting performance.

2. It is necessary to compare the proposed method with other existing approaches (e.g. with a benchmark dataset) to prove the proposed one is better than the others. Did methods, such as NMF, NMFHD, PA, RA, etc., shown in Table 5 propose by others? If yes, please give appropriate citations or notes in Table 5.

3. The abstract can be more detailed. Readers will appreciate the detailed information on the importance, innovation, and contributions of this research.

4. We also suggest the authors release their machine learning models or source codes for further academic studies in the future.

5. Please define the full name of NMFHD.

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7. 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 #4: No

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[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.]

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.

Predicting Adverse Drug Effects: A Heterogeneous Graph Convolution Network with a Multi-layer Perceptron Approach

PONE-D-22-08273R2

Dear Dr. Shih,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jinn-Moon Yang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #4: (No Response)

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7. 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 #4: No

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