Dear Dr. Abeel,

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Kind regards,

Edwin Hlangwani, PhD

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?

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: No

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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.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

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: This is a technically solid and innovative manuscript with strong potential impact. The benchmarking framework and demonstration of predicted meta-omics in phenotype classification are significant contributions. Before acceptance, I recommend revisions:

Major Concern Regarding Practical Implications and Conclusions:

The ultimate conclusion provided in the manuscript is currently vague and unsatisfying, particularly regarding the implications for practical microbiome research and disease prediction. Specifically:

- Clarify Practical Guidance: It is unclear whether researchers are advised to rely on predicted meta-omics values rather than experimental ones, particularly in the context of disease (IBD) classification. Do the authors suggest that predicted meta-omics data can genuinely substitute experimental multi-omics data in clinical or research settings, or is this merely a complementary tool?

- Single-omics vs Multi-omics: The manuscript does not clearly state if researchers would gain more from investing in collecting comprehensive multi-omics datasets or if predicted data from readily available (single-omics) sources provide sufficient insight.

Recommendation:

- Please explicitly state the authors’ recommendation. For example, should researchers confidently use predicted values in lieu of experimental multi-omics data for tasks like IBD classification, or is the main utility to augment existing datasets?

- Discuss explicitly under what conditions researchers should prioritize collecting multi-omics datasets versus using predicted meta-omics, considering trade-offs in cost, complexity, accuracy, and reliability.

Addressing these points will significantly enhance the clarity, impact, and practical utility of the manuscript

Technical Soundness and Support for Conclusions

- Protein prediction (mPx) remains weak (average correlations ~0.4), which the authors acknowledge, but further discussion of the biological and technical reasons for this limitation would strengthen the conclusions.

- The biological interpretation of consistently well-predicted features (e.g., glutamate dehydrogenase and RNA polymerase, page 18) is only briefly touched upon; more in-depth discussion of why these features are robustly predictable would improve the manuscript.

Statistical Analysis

- For classification, the focus is on accuracy. Reporting additional metrics (AUC, precision, recall, F1) would give a fuller picture, particularly given class balance issues acknowledged on page 26.

- Metaproteomics seems to suffer from data availability. Would be nice to know if this could have been overcome by using simulated data.

Clarity and Writing Quality

- Need to stick to multi-meta-omics (page 18) or multiple meta-omics (page 3). Very inconsistent throughout the manuscript.

- Figure 3 implies that there should be yellow for the metabolomics but there is no yellow in the chart.

- Page 10 starts with a capital A when the previous sentence on page 9 was not complete.

Reviewer #2: Suggestions for improvement

Study Design & Methodology:

-Clarify train/test splits, 10-fold cross-validation, and detailed preprocessing (imputation, normalization).

-Discuss findings’ generalizability, specifically if IBDMDB, CRC, and ESRD datasets adequately represent diverse microbiome contexts.

-Explain how class imbalance was addressed beyond downsampling (e.g., stratified weighting).

-Provide statistical testing for performance comparisons (e.g., significance of differences between elastic net regression and other models, sparse encoder–decoder, feed-forward neural network, and random forest in predicting meta-omics layers).

-Consider including a brief statement on computational resource usage (e.g., training runtime, GPU specifications) for reproducibility completeness.

-Consider a short paragraph on ethical/data governance implications of AI-predicted biological data, aligning with PLOS ONE’s “broader impact” expectations.

Results & Discussion:

-The results are clearly presented with well-labelled figures and logical progression from model benchmarking to biological application.

-The finding that a “core subset” of features is consistently well-predicted is an important insight.

-Discuss biological interpretability on how these predictions can inform hypothesis generation, rather than only classification tasks.

-Add enrichment or pathway analysis for the top predicted transcripts, proteins, and metabolites.

-Quantify inter-model variability statistically rather than descriptively.

Conclusion:

-The conclusions align with evidence, and the manuscript shows predicted meta-omics features can replace experimental data for tasks like IBD classification.

-While limitations like sample size are noted, further discussion on how larger multi-omics repositories could enhance predictive performance would be beneficial.

-Expand on the future applicability of predicted meta-omics (e.g., in personalized medicine or microbial ecology).

-Add a paragraph on the ethical/data governance implications of AI-predicted biological data, per PLOS ONE’s “broader impact” guidelines.

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Reviewer #1: Yes: Boahemaa Adu-OppongBoahemaa Adu-OppongBoahemaa Adu-OppongBoahemaa Adu-Oppong

Reviewer #2: No

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Predicted meta-omics: a potential solution to multi-omics data scarcity in microbiome studies

PONE-D-25-29865R1

Dear Dr. Abeel,

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.

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Kind regards,

Edwin Hlangwani, PhD

Academic Editor

PLOS One

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