E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides

Si-Cheng Wang

doi:10.1371/journal.pone.0300125

Peer Review History

Original SubmissionMay 30, 2023
19 Jun 2023 Decision Letter - Shahid Akbar, Editor PONE-D-23-16655E-CLEAP: An Ensemble Learning Model for Efficient and Accurate Identification of Antimicrobial PeptidesPLOS ONE Dear Dr. Wang, 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 03 2023 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. 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If you have no competing interests, please state "The authors have declared that no competing interests exist.", as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now This information should be included in your cover letter; we will change the online submission form on your behalf. [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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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: 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 ****** 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: After evaluating the whole manuscript the following comments are needs to incorporated before any possible publication. 1. The literature review of the paper seems really poor, the authors are suggested to add the the latest computational models developed for antimicrobial peptides papers such as, LMPred, AMPlify, ClassAMP, AI4AMP, AmPEP, AMP-GSM,and many more in order to strengthen the literature review section. 2. As the author used ensemble learning model to increase the prediction of the model, however in the introduction section the authors didn't mentioned the effectiveness and motivation to use ensemble learning as provided in iAFPs-EnC-GA and iAtbP-Hyb-EnC models. 3. The authors are suggested to provide a paragraph related to smote oversampling with its proper algorithm. 4. The should provide the results of the model before and after applying smote . 5. The authors should provided the size of the training vector before training a machine learning model. 6. The results of the proposed model should be compared with existing models in terms of predictive results and time complexity. Reviewer #2: A) In the introduction section the author are advised to clearly mention the novelty of the proposed model. B) To measure the generalization power and overfitting of the proposed model, an independent dataset is highly mandatory. C) the ShAp and LIME analysis of the proposed model is required to evaluate the contribution of the extracted features . D) An extra section are needs to incorporated related to majority voting based ensemble learning. E) A pseudo code of the proposed model should be added. F) A github link should be provided in the manuscript regarding the used datasets and the codes of the proposed model. ****** 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: Yes: Dr. Hashim Ali ******** [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. https://doi.org/10.1371/journal.pone.0300125.r001
Revision 1
5 Feb 2024 Author Response Dear Professor, Thank you very much for giving us the opportunity to revise the paper. Based on the constructive review comments, we have substantially revised the paper as follows. Thank you again for your guidance. Yours sincerely, The Author Response to Reviewer 1 Comments 1）The literature review of the paper seems really poor, the authors are suggested to add the the latest computational models developed for antimicrobial peptides papers such as, LMPred, AMPlify, ClassAMP, AI4AMP, AmPEP, AMP-GSM, and many more in order to strengthen the literature review section. Thanks for your comment. I consider this to be a valuable suggestion. Following the reviewer's recommendation, I have included the AI4AMP, AmPEP, AMP-GMS, and AMPlify models to enhance the literature review. Change made: Page 1, Line 41-49; Page 22-23, Line 349-358 2)As the author used ensemble learning model to increase the prediction of the model, however in the introduction section the authors didn't mentioned the effectiveness and motivation to use ensemble learning as provided in iAFPs-EnC-GA and iAtbP-Hyb-EnC models. Thank you for your suggestion. I have revised the relevant sentence in line 54 and added references to support this point. I have elaborated on the motivation and effectiveness of using ensemble learning models. Change made: Page 3, Line 54-58; Page 23, Line 359-364 3)The authors are suggested to provide a paragraph related to smote oversampling with its proper algorithm. Thank you for your suggestion. I think this is an excellent suggestion. Following the reviewer's advice, we have incorporated additional content regarding SMOTE oversampling and its appropriate algorithms. Change made: Page 10-11, Line 181-187 4)The should provide the results of the model before and after applying smote. Thank you for your comment. As you have expressed concerns, the results can be presented more clearly. In accordance with your suggestion, I have added a comparison of the results of the E-CLEAP model before and after using the SMOTE algorithm, along with the inclusion of Figure 3 and Table 1. Change made: Page 11-12, Line 188-195 5)The authors should provided the size of the training vector before training a machine learning model. Thank you for your question. The author recognized this deficiency and, following your suggestion, provided the size of the training vector before training the machine learning model. Change made: Page 12, Line 197-198; Page 15, Line 226-227 6)The results of the proposed model should be compared with existing models in terms of predictive results and time complexity. Thank you very much for your professional review of the article. In the manuscript, I have provided a detailed comparison between the performance metrics of the E-CLEAP model, including ROC curves, AUC, ACC, and those of traditional SVM, DT, KNN, and Bayes classifiers. In the Discussion section, I have added a new paragraph to clearly articulate the superior performance highlighted by comparing the E-CLEAP model with traditional models. Change made: Page 21, Line 287-291 Response to Reviewer 2 Comments 1)In the introduction section the author are advised to clearly mention the novelty of the proposed model. Thank you for your valuable suggestion. I have incorporated the novelty of the proposed model into the introduction section. Change made: Page 4, Line 64-66 2)To measure the generalization power and overfitting of the proposed model, an independent dataset is highly mandatory. I highly appreciate your perspective and thank you for your professional review. Regarding the dataset I utilized, I would like to provide the following clarification: the test set is an independent dataset, and during the training process, I further split the training set into training and validation sets for five-fold cross-validation. 3)the ShAp and LIME analysis of the proposed model is required to evaluate the contribution of the extracted features. Thank you for your suggestion. I consider this to be a valuable suggestion. Following the reviewer's advice, I have incorporated LIME analysis to assess the contribution of features extracted by the E-CLEAP model. Regarding the choice of LIME analysis, I provide the following explanation: Multilayer Perceptrons (MLP) are non-tree models, and the support for the 'shap' library in Python is relatively weaker for non-tree models. Therefore, the 'lime' library is chosen for interpreting non-tree models. Change made: Page 19-20, Line 255-272 4）An extra section are needs to incorporated related to majority voting based ensemble learning. Thank you for your suggestion. As suggested by the reviewer, I have added a section in the methodology that provides a detailed explanation of why the soft voting mechanism was chosen and elaborates on the ensemble learning model with a majority voting mechanism. There are two common voting mechanisms, and I believe it is necessary to explain the rationale behind the choice. This adds a level of rigor to the methodology. Change made: Page 7, Line 114-123 5）A pseudo code of the proposed model should be added. Thank you very much for your suggestion. As you are concerned, pseudocode can make the model more clearly presented. I have added the pseudocode of my proposed model in the methodology section. Change made: Page 7-8, Line 123-124 6）A github link should be provided in the manuscript regarding the used datasets and the codes of the proposed model. I greatly appreciate your professional review of my article. Additionally, I have included a GitHub link in the abstract section, providing access to the dataset used and the code for the proposed model. Change made: Page 2, Line 23 Attachments Attachment Submitted filename: Respond reviewer.docx https://doi.org/10.1371/journal.pone.0300125.r002
22 Feb 2024 Decision Letter - Shahid Akbar, Editor E-CLEAP: An Ensemble Learning Model for Efficient and Accurate Identification of Antimicrobial Peptides PONE-D-23-16655R1 Dear Dr. Wang, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Shahid Akbar, PhD 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 Reviewer #2: All comments have been addressed ******** 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 Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 #1: Yes Reviewer #2: Yes ****** 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 Reviewer #2: Yes ****** 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: required comments are incorporated i have no further comments and therefore i recommend accept the paper Reviewer #2: Nil as all the changes are made. no further changes are required. Corrections and grammatical errors are removed as well. ****** 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 Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0300125.r003
Formally Accepted
29 Apr 2024 Acceptance Letter - Shahid Akbar, Editor PONE-D-23-16655R1 PLOS ONE Dear Dr. Wang, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Shahid Akbar Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0300125.r004

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