PONE-D-24-07765A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment culturesPLOS ONE

Dear Dr. Zhang,

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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

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

Reviewer #1: 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.

Reviewer #1: Yes

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

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Reviewer #1: 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: Dear authors,

thank you for submitting your research, it is very interesting and relevant work you do!

Overall, I like your submission. You explore the potential of ML methods improving the speed and potentially the quality of the assessment of probes for the presence of Campylobacter.

The introduction to the topic is clear and concise, so is the description of the employed data collection and ML methods.

As my expertise lies in the ML area, I will focus my comments on the corresponding parts of the submission. To summarize, I have major concerns with the training, testing and evaluation procedures of the ML methods, and a list of minor comments for improving the manuscript.

Major: ML methods

It is good to start with simple methods like logistic regression, SVM and RF. However, the evaluation should be thorough. Most importantly, it was not entirely clear how the training and hyperparameter selection was performed. To select the hyperparameters, one should perform a N-fold crossvalidation for each hyperparameter, e.g., split the training data into 80-20 random splits and train and test on different splits for each hyperparameter, then select the one with the lowest average test performance.

After this selection process, the method should be trained on the entire training data set again using the selected hyperparameters and then finally applied to the actual test set which was not used at all before.

It is essential the report error bars on the report classifier performances (Table 3). To that end, one could repeat the above procedure 5-10 (N) times and report mean test performance +- standard error of the mean (mean +- std / sqrt(N).

What is the dimensionality of the spectral data? If it is high, this might be an explanation for the relatively low performance of the classifiers. It might be worth exploring non-linear methods, e.g., neural-network based logistic regression. However, I see that the training data is limited and might not suffices for training neural networks.

Further questions and questions:

1) How exactly is the MPN calculated? In line 170 you mention “by applying probability theory”. I think this part definitively need more explanation, given that it seems to be the central assessment for the presence of Campylobacter. I am also wondering what it is needed at all. Why not report the presence of the bacteria as binary variable and take the average over probes?

2) How exactly is the correlation between spectral data (continuous, high-dimensional) and Campylobacter presence (binary) calculated?

3) RF should also use the class_weight="balanced" option because the number of positive and negative examples is not equal.

4) Table 3: the extraordinary high training accuracy is a strong sign for overfitting! Also, how can the test accuracy be 1.0 for test 5? This looks suspicious. Error bars would help here (see comment above).

5) Figure 5: Absolute numbers are difficult to interpret. Numbers should be relative, e.g., percentage or proportions.

6) The justification for using NSE for comparing the MPN predictions is not clear. Also, the explanations for how exactly NSE and the confidence intervals are calculated are missing. As a consequence, Figures 5 and 6 are unclear, e.g., why can NSE be negative and  what does that mean?

Minor comments:

line 15: first sentence seems grammatically off and difficult to parse

line 39: “-a”

line 49: change of font size.

line 58: MPN-PCR has not been introduced

line 60: AS/NZS not introduced

line 85: acronyms not introduced

line 89: Argument is not sound. How does this sentence follow from the previous sentence?

line 107: Typo “these”, “spp”

line 199: Unlikely that this is the correct reference for logisitic regression or the sigmoid function.

line 420: what are “good” correlations? I suggest to use a different more objective adjective.

Overall, I think that the ML training and evaluation part needs significant changes and more explanations. But once this is addressed, I think that the paper would be a valuable contribution.

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Reviewer #1: No

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A machine learning approach for rapid early detection of Campylobacter spp. using absorbance spectra collected from enrichment cultures

PONE-D-24-07765R1

Dear Dr. Zhang,

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.

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Kind regards,

Ricardo Santos

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

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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

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

Reviewer #1: Yes

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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

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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

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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: Dear authors, thank you for submitting a revised version of the manuscript! The point-to-point response to my comments is clear and addresses all the questions and concerns I had with the initial submission.

I have two more comments:

1) I want to encourage you to also make the code available online, e.g, on GitHub, to make your study more accessible and easier to reproduce.

2) I agree with you that your dataset is very interesting and relevant. I think it would be great to provide the dataset and the task description for predicting the presence of the bacteria on a competition platform like Kaggle. This will give you access to a large community of user that will try to solve the task in different ways, giving you inspiration for your future work on this topic.

Best wishes,

Jan

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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

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