PONE-D-25-02109SEANN: A Domain-Informed Neural Network for Epidemiological InsightsPLOS ONE

Dear Dr. Cazabet,

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 Aug 22 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 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,

Vijay Kumar

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse.

3. Thank you for stating in your Funding Statement:

“The research is partially funded by the Meersens Company, financing the salary of the Ph.D student (first author) (https://meersens.com). The sponsor played no specific role in the research related to this manuscript.”

Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.

Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.

4. Thank you for stating the following financial disclosure:

“The research is partially funded by the Meersens Company, financing the salary of the Ph.D student (first author) (https://meersens.com). The sponsor played no specific role in the research related to this manuscript.”

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

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.

5. We note that your Data Availability Statement is currently as follows: All relevant data are within the manuscript and its Supporting Information files.

Please confirm at this time whether or not your submission contains all raw data required to replicate the results of your study. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods (https://journals.plos.org/plosone/s/data-availability#loc-minimal-data-set-definition).

For example, authors should submit the following data:

- The values behind the means, standard deviations and other measures reported;

- The values used to build graphs;

- The points extracted from images for analysis.

Authors do not need to submit their entire data set if only a portion of the data was used in the reported study.

If your submission does not contain these data, please either upload them as Supporting Information files or deposit them to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories.

If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access.

[Note: HTML markup is below. Please do not edit.]

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: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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: No

Reviewer #3: Yes

**********

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: Yes

Reviewer #3: Yes

**********

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: General Comments:

The manuscript presents a novel and relevant approach by introducing SEANN, a domain-informed neural network model that leverages pooled effect sizes (PES) to enhance prediction and interpretation in epidemiological datasets. The work is timely and contributes to the growing intersection of machine learning and health sciences, especially in low-data settings. The manuscript is written in good English; however, it requires minor revisions before it can be accepted for publication.

Specific Comments:

1. The manuscript, while clearly written, lacks consistent formatting throughout. Please ensure that headings, equations, and paragraphs follow the journal’s style guide. Several in-line equations are not properly spaced and disrupt the flow of reading.

2. Authors should check for the typographical errors such as in the Introduction section: “Odd Rations (ORs)” should be revised to “Odds Ratios (ORs).”

3. Authors should check for citing relevant citations in the text for e.g. On Page 5, the following line is missing a relevant citation:

"For a vector V of SRCs derived from the literature, with vi ∈ V, the ith element of V, the training loss term Lmeta is defined as..."

Cite appropriate literature to support the use of the custom loss formulation.

4. While the analysis appears comprehensive, the discussion lacks sufficient depth. It is recommended that the authors elaborate more on how their results compare with prior methods and explain the implications of improved generalizability in real-world epidemiological studies.

Reviewer #2: 1.This manuscript introduces a novel approach, SEANN (Summary Effects Adapted Neural Network), which integrates pooled effect sizes (PESs) such as Odds ratios, Risk ratios, and standardized regression coefficients directly into deep neural network training. This is a unique and timely contribution to informed machine learning, particularly relevant to fields like epidemiology, where data is often limited or noisy. SEANN helps improve both predictive performance and interpretability by embedding prior scientific knowledge into model training.

2.The methodology is technically solid and clearly presented. The paper explains how PESs are embedded via custom loss functions and how weighting is derived based on confidence (sample size). The treatment of three common types of PESs is thorough. However, while the math is sound, the use of complex symbols and notations (Example: perturbation steps, arrow symbols) might challenge readers outside machine learning. Adding a simpler example with numeric inputs could help clarify the non-expert audiences.

3.The experiments are well-structured and simulate realistic epidemiological challenges noise, missing data, and confounding. The use of synthetic data is reasonable for isolating the effects of the method. SEANN consistently outperforms standard DNNs in terms of both prediction and interpretability, especially when data is imperfect. The use of the SHAP metric to assess alignment with true relationships is particularly valuable. However, the study would benefit from including comparisons with other informed machine learning models, like PGNN or DANN, or at least discussing their relative merits.

4.SEANN enhances the interpretability of model predictions, aligning well with real-world needs in health research. For example, it corrects misleading associations when variables are confounded or missing. While the figures and quantitative results are strong, some brief narrative explanations of what those corrections mean in practice, especially for health researchers, would make the work more engaging and accessible.

5.This is a strong and original piece of work. To improve it further, the authors should consider: Including a real-world dataset example.

Reviewer #3: While the paper presents a creative idea — integrating domain knowledge in the form of pooled effect sizes into a neural network via a modified loss function — the manuscript ultimately lacks sufficient scientific rigor and practical relevance to warrant publication in PLOS ONE. My detailed concerns are as follows:

1. Lack of Real Data: The manuscript claims to improve epidemiological modeling, yet it contains no experiments on real-world epidemiological datasets. Synthetic toy examples (e.g., fish, mercury, stress, BMI) are insufficient to demonstrate the utility of the approach. Without validation on observational datasets from real health studies, the results cannot be trusted or generalized.

2. Overclaiming Practical Use: The term “epidemiological insights” in the title and abstract is misleading. No insights about disease risk, exposure, or population health are produced in this study. The method is demonstrated only in artificial settings, using pre-defined effects, which defeats the purpose of “insight” discovery.

3. Weak Baselines and Limited Evaluation: The authors compare SEANN only to an “agnostic DNN” — essentially a DNN without the PES-informed loss — and omit any comparison to:

- Classical epidemiological methods (e.g., regularized logistic regression with prior constraints),

- Knowledge-regularized models in causal inference,

- Other informed machine learning models in epidemiology (e.g., those incorporating DAGs or monotonic constraints).

The absence of meaningful baselines diminishes the credibility of the claimed improvements.

4. Unconvincing Justification of PES Integration: The proposed method modifies the loss function using hand-selected perturbation values (e.g., h=1 or h=1/vi), but these choices are not theoretically or empirically justified. There is also no sensitivity analysis regarding how these perturbations affect training stability or generalization. Furthermore, integrating PES values — which are often heterogeneous and context-dependent — as “hard-coded” truth in synthetic experiments does not reflect how they behave in actual epidemiological studies.

5. Limited Contribution to Machine Learning or Epidemiology: While the paper claims novelty in the integration of PESs into neural networks, the broader field of informed ML has explored similar loss regularization strategies with more robust theory and applications. This work does not meaningfully advance the methodological or epidemiological literature beyond existing approaches.

6. Figures and Tables Are Minimal and Not Informative Enough: The visualizations do not adequately explain how SEANN outperforms baselines. Key graphs (e.g., SHAP plots) are not contextualized with respect to meaningful health variables, and only small performance differences are observed in some metrics.

**********

6. 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

**********

[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.

SEANN: A Domain-Informed Neural Network for Epidemiological Insights

PONE-D-25-02109R1

Dear Dr. Cazabet,

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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support.

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,

Vijay Kumar

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

As I have considered that the authors appropriately addressed all the comments from the reviewers, the manuscript can be accepted.

Reviewers' comments: