Machine learning predicts translation initiation sites in neurologic diseases with nucleotide repeat expansions

Alec C. Gleason; Ghanashyam Ghadge; Jin Chen; Yoshifumi Sonobe; Raymond P. Roos

doi:10.1371/journal.pone.0256411

Peer Review History

Original SubmissionAugust 3, 2021
31 Jan 2022 Decision Letter - Thippa Reddy Gadekallu, Editor PONE-D-21-24907Machine learning predicts translation initiation sites in neurologic diseases with expanded repeatsPLOS ONE Dear Dr. Roos, 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. ============================== ACADEMIC EDITOR: Based on the comments from the reviewer and my own assessment I recommend major revisions for the article. ============================== Please submit your revised manuscript by Mar 17 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. 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The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf” 2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. [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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: 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. 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(Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The proposed work presents a machine learning approach for neurologic diseases. It is an exciting research area. However, the following major changes are required. • I recommend authors to add a separate section to describe the novelties and contributions of the proposed work in detail. • The comparison of the proposed approach with state of the art is missing. Therefore, a detailed workflow describing the proposed approach is required. • I recommend authors to use the benchmark dataset and perform similar experiments and discuss the comparison. If a benchmark dataset is not available, authors can create a customized dataset and describe the data collection process in detail. • The literature review carried out for the proposed work is outdated and needs the referral of some of the latest research works published in the last three years such as https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12899, https://www.frontiersin.org/articles/10.3389/fpubh.2021.762303/full, https://www.mdpi.com/1424-8220/20/20/5780. • I recommend authors to add a layered architecture and detailed work to give more clarity to readers about the proposed system. • I recommend authors to add limitations in detail(instead of abstract information) of the proposed system and future directions. • The resolution of all figures is a concern. I recommend authors to redraw most of the images to match the journal standards. • All tables should be symmetrical and should follow a similar formatting style. • All the equations should be written using a professional equation editor and should use a similar formatting style and numbering. ****** 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: Yes:** Sharnil Pandya [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.0256411.r001
Revision 1
1 Mar 2022 Author Response We thank the academic editor and the reviewer for their comments. Below, we explain our revisions for each point raised. We hope that our updated manuscript now satisfies all criteria and will be suited for publication. Academic Editor 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf” We have made necessary adjustments to ensure that the manuscript meets PLOS ONE’s style requirements. We followed the requirements for file naming as well. 2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. That is correct. We have not changed our Data Availability statement. Please note that we have also added a file to “Supporting information.” Reviewer 1. I recommend authors to add a separate section to describe the novelties and contributions of the proposed work in detail. We have added a subsection under the “Introduction” header called “Novelties and Contributions of the Proposed Work” where we describe the novelties and contributions of the proposed work in detail. 2. The comparison of the proposed approach with state of the art is missing. Therefore, a detailed workflow describing the proposed approach is required. We have added a subsection in which we compare the performance of our proposed machine learning algorithm to state of the art convolutional neural networks. We further added figures 9 and 15, which outline the workflow of our proposed machine learning algorithms and software. 3. I recommend authors to use the benchmark dataset and perform similar experiments and discuss the comparison. If a benchmark dataset is not available, authors can create a customized dataset and describe the data collection process in detail. We have added a section in which we compare the performance of the random forest machine learning algorithm to an optimized convolutional neural network using the same training and test datasets. 4. The literature review carried out for the proposed work is outdated and needs the referral of some of the latest research works published in the last three years such as https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12899, https://www.frontiersin.org/articles/10.3389/fpubh.2021.762303/full, https://www.mdpi.com/1424-8220/20/20/5780. We thank the Reviewer for this suggestion. We have reviewed and cited more research from the last three years, including two of the suggested papers. 5. I recommend authors to add a layered architecture and detailed work to give more clarity to readers about the proposed system. We have added Fig 9, which depicts the architecture of the software we deploy to visualize predictions. We have also included Fig 15, which details the structure of the random forest classifier incorporated into the system. 6. I recommend authors to add limitations in detail (instead of abstract information) of the proposed system and future directions. We have added a “Limitations” subsection under “Discussion” which details the limitations of our proposed system. We address future directions in the rest of our discussion. 7. The resolution of all figures is a concern. I recommend authors to redraw most of the images to match the journal standards. High resolution TIFF images (600 dpi) were originally uploaded with the manuscript; however, they appear blurry on the preprint for reasons beyond our control. The ‘preview pdf’ version of the preprint better displays the images, however, the images have been stretched out. 8. All tables should be symmetrical and should follow a similar formatting style. All tables are now symmetrical. We have adjusted Table 2 to have a similar formatting style to Table 1. 9. All the equations should be written using a professional equation editor and should use a similar formatting style and numbering. All equations have been rewritten and numbered using professional equation editor MathType. Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0256411.r002
17 May 2022 Decision Letter - Thippa Reddy Gadekallu, Editor Machine learning predicts translation initiation sites in neurologic diseases with nucleotide repeat expansions PONE-D-21-24907R1 Dear Dr. Roos, 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, Thippa Reddy Gadekallu Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0256411.r003
Formally Accepted
20 May 2022 Acceptance Letter - Thippa Reddy Gadekallu, Editor PONE-D-21-24907R1 Machine learning predicts translation initiation sites in neurologic diseases with nucleotide repeat expansions Dear Dr. Roos: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Thippa Reddy Gadekallu Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0256411.r004

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