PONE-D-22-04434A machine learning approach using k – mode clustering and random forest classification to model groups of production parameters and bulk tank milk antibody status of two major internal parasites in dairy cowsPLOS ONE

Dear Dr. Oehm,

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PLOS ONE

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2. Thank you for stating the following financial disclosure:

“Farm visits and data collection in the context of the underlying cross-sectional study were financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) grant number 2814HS008.”

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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: No

Reviewer #2: Yes

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

Reviewer #1: No

Reviewer #2: 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: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: 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: In the title the authors write "k-mode clustering" but in the method section it states "K-medoids" without mentioning "k-mode"(line 207). It should be noted that these terms and the corresponding algorithms are very different.

title - What are these "groups of production parameters and bulk tank milk antibody status"? I don't see any groups of parameters reported. The groups should be of the farms according to the context.

line 67, This sentence is confusing. what is the difference between the terms "cluster" and "group" in this sentence. For clustering analyses, the cluster is the group.

line 191, "Missing values were excluded from" -> Farms with missing values were excluded

Line 192, "Apart from" could mean "except for" or " in addition to". Is antibody status in or not in the model? I think " in addition to" is better here.

line 213 "PAM replaces centroids with medoids"-> PAM replaces means with medoids

line 218 "Classification of Clusters by means of Random Forest". Clustering analyses is for unsupervised classification, I don't see why clusters need to be classified again. Besides, there are only 2 cluster reported (figure 1), how could these be classified again?

Reviewer #2: The manuscript describes a strong and novel work that used k-mode clustering to separate the internal features of the different diary farms and used random forest to analyze features that are important to the cluster separation. The authors used very rigorous statistical approaches in data acquisition, cleaning, aggregation, and analysis. The authors clearly demonstrated the methodology and results, and come to a well-supported conclusion that the antibody status of two major internal parasites in diary cows strongly affects the milk yield and nutrition content. The work thoroughly covered a vast number of farms over major diary production regions in Germany. The conclusion was also supported by relevant studies and field. In addition to the scientific insight that brings direct economical benefits, the manuscript demonstrated the feasibility of using un-supervised methods to perform non-biased analyze on the topic. Therefore, I strongly recommend publication of the work.

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Reviewer #1: No

Reviewer #2: Yes: Weihao Ge

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PONE-D-22-04434R1A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cowsPLOS ONE

Dear Dr. Oehm,

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.

The reviewers have suggested some minor changes to improve the manuscript quality.

Please submit your revised manuscript by Aug 05 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,

Rebecca Lee Smith, D.V.M., M.S., Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please 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.

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Please consider the recommendations of the reviewer.

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Reviewers' comments:

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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 #2: All comments have been addressed

Reviewer #3: (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 #2: Yes

Reviewer #3: Yes

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

Reviewer #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: Yes

Reviewer #3: 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 #2: The manuscript is strong. The work applied an unsupervised approach that naturally separated the studied farms by positive/negative of common bacterial antibodies. With further feature analysis, the work identified the variables that are associated with infections as well as milk yield. The work is of important direct economical impact and is based on sound statistics. Therefore, I recommend the publication of the work.

Reviewer #3: The results presented in this paper are interesting and of practical importance in identifying biologically relevant differences between farms positive for F. hepatica or O. ostertagi, respectively and negative farms using farm-level bulk tank milk. This important in the veterinary field.

The revisions suggested are minor and involve some additional explanations, plots, and/or statistical procedures.

Minor Revisions and comments:

1. p. 9, line 200: The authors have chosen the Gower’s distance matrix for clustering stating that it is the most common distance matrix for a mix of categorical and continuous values and cite 2 papers (28, 29). It is probably the first distance measure proposed in 1971, but there are currently many more choices. An unsupervised random forest can be used to obtain a proximity matrix that could also be used for clustering. A random forest easily handles mixed types of data. See Conrad and Bailey 2015 PLoS One paper on clustering Cystic Fibrosis patients, for an example. It will probability not make much difference in the clustering results, but the authors should be aware of other measures.

2. p. 15, line 303: For all cluster analyses, the silhouette method selected 2 clusters to be optimal …

Authors should provide an average silhouette plot that shows that 2 clusters are optimal. This is because in the Cluster Analyses Section and the description of Fig 1 (F. hepatica cluster analyses) and Fig 2 (O. ostertagi cluster analyses) there were often a “majority cluster” and a “mixed cluster”. It would be very interesting to know that if k=3 clusters were chosen (assuming that the average silhouette plot showed that 2 or 3 clusters were reasonable choices), if the mixed cluster divided into 2 additional more homogeneous or identifiable clusters.

3. p. 27-28 Section: Classification of Clusters by means of Random Forest

Results of a supervised random forest and variable importance plots are given in

Fig 3 (F. hepatica for North (Fig 3 A) and South (Fig 3 B)) and Fig 4 (O. ostertagi for North (Fig 4 A) and East (Fig 4 B) and South (Fig 4 C)).

Statements are made about what variable are “most important”. This is obtained from the ranking of the variables. There is an rfPermute package that will perform a permutation test and provide estimated permutation p-values for the importance metric of the random forest by permuting the response variable. This would allow the authors to make statements about important variables with a reasonable p-value cut-off. This will strengthen the statements about identifying variables that are most important in classifying or separating the clusters.

4. p. 2: In the Abstract it is stated

“Across all study regions, co-infections with F. hepatica or O. ostertagi, respectively, farming type, and pasture access appeared to be the most important factors discriminating clusters (i.e.39 farms). Furthermore, herd size, BCS, and stage of lactation were relevant criteria distinguishing clusters.” Permutation p-values will allow the statement to be made based using a p-value cut-off.

5. Discussion:

p. 29, line 477: Authors state “Moreover, the current work is probably the first of its kind to implement a k-medoids cluster algorithm and PAM.” Please see and cite:

DJ Conrad, BA Bailey, 2015

Multidimensional clinical phenotyping of an adult cystic fibrosis patient population

PLoS One 10 (3), e0122705

DJ Conrad, J Billings, C Teneback, J Koff, D Rosenbluth, BA Bailey, ..., 2021

Multi-dimensional clinical phenotyping of a national cohort of adult cystic fibrosis patients

Journal of Cystic Fibrosis 20 (1), 91-96

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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.

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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 #2: Yes: Weihao Ge

Reviewer #3: No

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A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows

PONE-D-22-04434R2

Dear Dr. Oehm,

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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Rebecca Lee Smith, D.V.M., M.S., Ph.D.

Academic Editor

PLOS ONE

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