Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate-Induced Rats

Round 1

Reviewer 1 Report

Major:

Methodology: line 115, Experimental groups could be based on the treatment not based on the expected observation. So, the experimental group throughout the manuscript could be Control and MTX treated groups.

Discussion, Line number 378 – Could explain the more about the particular transport vulnerable to toxic injury

How the authors are confirming the cell necrosis just by H and E staining. Using appropriate markers/ staining methods would be a better justification.

Minor comments

The article is well written. However, there are few corrections needed

 

1.       Including LncRNA in the key word would be great

2.       Line number: 177 – should be “Buffer” not Buf-fered

3.       Figure 1. Arrow could be bigger

4.       Table 1. Instead of starting, initial or baseline would be appropriate word, similarly end could be experimental end point

5.       Table 2: Nephrotoxicity could be replaced with MTX treatment

6.       Figure 7. Legend should be with proper scientific terminology, Example, down regulated, up regulated and unaltered

7.       Conclusion sentences are unclear, better to re-write.

 

Author Response

Dear Reviewer,

We made all corrections suggested. We really appreciate all the advice you gave us. Therefore, the manuscript was re-evaluated, and grammatical errors were corrected to the best we could. We thank you so much for the suggestions related to our paper. We look forward to hearing your positive decision.

Author Response File: Author Response.docx

Reviewer 2 Report

Manuscript Title: Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate Induced Rats

 

Dear Editor,

 

I have carefully reviewed the manuscript titled "Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate Induced Rats." The study aims to carry out bioinformatic analysis of lncRNA data obtained from genomic analysis of kidney tissue samples in rats with nephrotoxicity induced by methotrexate (MTX). The manuscript also focuses on identifying potential biomarkers for nephrotoxicity and providing the interpretability of the model using explainable artificial intelligence methods.

However, there are a few areas that need to be addressed:

 

-The interpretation and implications of the identified lncRNAs as potential biomarkers should be further discussed. 

-How do these biomarkers contribute to the understanding of nephrotoxicity mechanisms, and how can they be leveraged for future drug development studies?

-To enhance the validity and translatability of the results, the authors should explain the effectiveness of the identified biomarkers.

-Figure 5 and Figure 6 could be improved for better clarity and readability. Providing clear labels, appropriate scales, and concise yet informative captions can improve the overall quality of the figures.

 

* I kindly suggest using clearer expressions and correcting grammar mistakes in the provided all text.

For example:

This study has some limitations. This study was carried out with the data obtained from the mouse experiment and lays the groundwork for future studies. However, human studies are needed to confirm the results so that the results of the study can be generalized and used in potential drug development studies.

 

-This study has certain limitations. It was conducted using data obtained from mouse experiments and serves as a foundation for future research. However, it is essential to conduct human studies to validate the findings and enable the generalization of the results. These human studies would also facilitate their application in potential drug development studies.

 

I recommend that it be considered for publication after the authors have adequately addressed the concerns raised.

 

Sincerely,

 

Manuscript Title: Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate Induced Rats

 

Dear Editor,

 

I have carefully reviewed the manuscript titled "Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate Induced Rats." The study aims to carry out bioinformatic analysis of lncRNA data obtained from genomic analysis of kidney tissue samples in rats with nephrotoxicity induced by methotrexate (MTX). The manuscript also focuses on identifying potential biomarkers for nephrotoxicity and providing the interpretability of the model using explainable artificial intelligence methods.

However, there are a few areas that need to be addressed:

 

-The interpretation and implications of the identified lncRNAs as potential biomarkers should be further discussed. 

-How do these biomarkers contribute to the understanding of nephrotoxicity mechanisms, and how can they be leveraged for future drug development studies?

-To enhance the validity and translatability of the results, the authors should explain the effectiveness of the identified biomarkers.

-Figure 5 and Figure 6 could be improved for better clarity and readability. Providing clear labels, appropriate scales, and concise yet informative captions can improve the overall quality of the figures.

 

* I kindly suggest using clearer expressions and correcting grammar mistakes in the provided all text.

For example:

This study has some limitations. This study was carried out with the data obtained from the mouse experiment and lays the groundwork for future studies. However, human studies are needed to confirm the results so that the results of the study can be generalized and used in potential drug development studies.

 

-This study has certain limitations. It was conducted using data obtained from mouse experiments and serves as a foundation for future research. However, it is essential to conduct human studies to validate the findings and enable the generalization of the results. These human studies would also facilitate their application in potential drug development studies.

 

I recommend that it be considered for publication after the authors have adequately addressed the concerns raised.

 

Sincerely,

 

Author Response

Dear Reviewer,

We made all corrections suggested. We really appreciate all the advice you gave us. Therefore, the manuscript was re-evaluated, and grammatical errors were corrected to the best we could. We thank you so much for the suggestions related to our paper. We look forward to hearing your positive decision.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Dear Editor,

I hope this letter finds you well. I have had the opportunity to review the revised manuscript titled "Nephrotoxicity Development of a Clinical Decision Support System Based on Tree-Based Machine Learning Methods to Detect Diagnostic Biomarkers from Genomic Data in Methotrexate-Induced Rats," which has been resubmitted to Applied Sciences. I appreciate the authors' prompt response to the previous review and their efforts in addressing the concerns raised. Based on the revisions made, I am pleased to recommend accepting the manuscript for publication in Applied Science. Thank you for the opportunity to review this revised manuscript, and I commend the authors for their efforts in addressing the concerns raised during the review process. I believe this work will make a valuable contribution to the field and will be of interest to the readers of Applied Sciences.

 

Sincerely,