Hypergraph modeling of complex interactions: Applications from human musculoskeletal structures to complex system dynamics

Hiroko Yamano; Shu Liu; Fujio Toriumi

doi:10.1371/journal.pone.0310189

Peer Review History

Original SubmissionMay 21, 2024
1 Jul 2024 Decision Letter - Hocine Cherifi, Editor PONE-D-24-20578Hypergraph modeling of complex interactions: Applications from human musculoskeletal structures to complex system dynamicsPLOS ONE Dear Dr. yamano, 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 15 2024 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, Hocine Cherifi Academic Editor PLOS ONE Journal Requirements: 1. When submitting your revision, we need you to address these additional requirements. 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 2. Thank you for uploading your study's underlying data set. Unfortunately, the repository you have noted in your Data Availability statement does not qualify as an acceptable data repository according to PLOS's standards. At this time, please upload the minimal data set necessary to replicate your study's findings to a stable, public repository (such as figshare or Dryad) and provide us with the relevant URLs, DOIs, or accession numbers that may be used to access these data. For a list of recommended repositories and additional information on PLOS standards for data deposition, please see https://journals.plos.org/plosone/s/recommended-repositories. 3. We notice that your supplementary tables are included in the manuscript file. Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list. 4. 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. [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: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: N/A ****** 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: No ****** 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: This paper focuses on utilizing graphs and hypergraphs to model the complex system of human musculoskeletal structures. Additionally, it compares the performance of simple graph and hypergraph-based modeling methods using various existing representation learning techniques. The experimental results demonstrate that hypergraph modeling outperforms simple graph modeling. Overall, this is a well-conceived application and exploration paper that investigates the use of graph embeddings in the biomedical domain. I have a few comments to help the authors improve their paper: 1. It would be beneficial to employ more simple graph embedding and hypergraph embedding models (not just three) on the constructed graphs and benchmark their performance. 2. The method of constructing a hypergraph from a bipartite graph is similar to that described in the paper "Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks." 3. There are two methods for transforming between simple graphs and hypergraphs, star and clique expansion. The authors should mention these methods and consider employing UniGNN on their datasets and tasks. See "UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks." 4. Another relevant paper proposes a Line Expansion from hypergraphs. The authors could consider adding this information to their paper: "Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion." Reviewer #2: This paper models the complex many-to-many interactions between muscles and bones as a hypergraph. The embeddings of the nodes are then learned using two graph-based and one hypergraph-based unsupervised embedding methods. The authors analyze the results based on the Euclidean distances between the obtained embeddings. While the paper presents an interesting approach to modeling human musculoskeletal structures as hypergraphs, there is room for improvement. Strenghts. - The rationale for modeling the musculoskeletal structure as hypergraphs is well motivated. - The authors clearly explain how their appraoch is distinct from previous approaches. - The figures effectively describe the concepts discussed. Weaknesses and Questions. - In the introduction, the authors mention one-mode projection. Is this equivalent to clique expansion? - The datasets used in the paper are too small. Please consider using larger datasets if possible. - Only a small number of embedding methods are employed. Other graph-based methods such as node2vec and hypergraph-based methods such as hyper2vec or VilLain could also be considered. Are the results consistent when using these additional methods? Please refer to the references below. - The embedding methods discussed can also be applied in star expansion. How does that impact the results? - While the analysis of the results using the embedding methods is the main contribution of the paper, the discussion of the methods themselves is overly detailed. I recommend simplifying the description of the methods and focusing more on the experimental resutls and analyses. - For better readability, please structure the discussion more clearly. I recommend the authors revise the manuscript to enhance clarity and focus. References. - node2vec: Scalable feature learning for networks (KDD 2016) - Hyper2vec: Biased random walk for hyper-network embedding (DSAFAA workshop 2019) - VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation (WWW 2024) ****** 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: No ******** [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.0310189.r001
Revision 1
17 Aug 2024 Author Response Reviewer #1: This paper focuses on utilizing graphs and hypergraphs to model the complex system of human musculoskeletal structures. Additionally, it compares the performance of simple graph and hypergraph-based modeling methods using various existing representation learning techniques. The experimental results demonstrate that hypergraph modeling outperforms simple graph modeling. Overall, this is a well-conceived application and exploration paper that investigates the use of graph embeddings in the biomedical domain. I have a few comments to help the authors improve their paper: 1. It would be beneficial to employ more simple graph embedding and hypergraph embedding models (not just three) on the constructed graphs and benchmark their performance. >> Following your suggestion, we employed five additional embedding methods to compare their performance. The main differences between the 8 methods, such as network type (pairwise / hypergraph), embedding (proximity / structure), and learning (supervised / unsupervised) criteria, are shown in Table 3. 2. The method of constructing a hypergraph from a bipartite graph is similar to that described in the paper "Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks." >> Thank you for your informative proposal. We have reviewed the proposed article and agree with the reviewer that we have a similar understanding with the authors about transforming a bipartite network into a hypergraph. We have included this in the introduction, citing the article as follows: "Although some researchers have employed similar approaches to construct a hypergraph from a bipartite graph in a more sophisticated way, such as the multiplex bipartite network embedding \\cite{xue2021multiplex}, to the best of our knowledge there are few applications to musculoskeletal networks." 3. There are two methods for transforming between simple graphs and hypergraphs, star and clique expansion. The authors should mention these methods and consider employing UniGNN on their datasets and tasks. See "UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks." >> Thank you for your insightful suggestion. We explained why we chose the clique expansion for pairwise based applications and described the correspondence between the star expansion and the bipartite network as follows. We also learned how UniGNN integrates the information of the hyperedges without using clique expansion, and employed UniSAGE, which is one of the methods in UniGNN. We chose UniSAGE because it is the default method in the four embedding methods collectively referred to as UniGNN. "In this study, we used the clique expansion technique, which is equivalent to the muscle-centric one-mode projection of the bipartite network of muscles and bones. Since the clique expansion forms a complete graph connecting all nodes within the same hyperedge, it is equivalent to the one-mode projection connecting all nodes that have a common neighbor, i.e., a hyperedge. We chose the clique expansion (one-mode projection) over the star expansion because it is more suitable for applications that focus on the relationships between muscles. In star expansion, a new central node is introduced for each hyperedge that connects to the associated muscles, which is equivalent to reverting to the original bipartite network of muscles and bones. This can obscure the direct relationships between muscles by shifting some of the focus to the central nodes (bones), thus detracting from a muscle-centric analysis." 4. Another relevant paper proposes a Line Expansion from hypergraphs. The authors could consider adding this information to their paper: "Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion." >> We surveyed the recommended paper and interested in the Line Expansion method, which concerns the information loss in hypergraph transformation, overcoming the limitation of clique and star expansion. However, in the biological settings of musculoskeletal network in this study, the nodes and edges should not be divided into multiple parts, because the nodes and edges in our study is not only the relationships, but also the physical connections of real muscles and bones. Therefore, for the same reason with clique and star expansions, we think that Line Expansion also loses the hypergraph nature by its expansion. However, given the importance of clarifying the expansion methods, we have cited the recommended paper in the sentence below. "There are basically two methods for transforming hypergraphs into pairwise networks; star and clique expansions and their generalization \\cite{yang2022semi}. " ======= Reviewer #2: This paper models the complex many-to-many interactions between muscles and bones as a hypergraph. The embeddings of the nodes are then learned using two graph-based and one hypergraph-based unsupervised embedding methods. The authors analyze the results based on the Euclidean distances between the obtained embeddings. While the paper presents an interesting approach to modeling human musculoskeletal structures as hypergraphs, there is room for improvement. Strenghts. - The rationale for modeling the musculoskeletal structure as hypergraphs is well motivated. - The authors clearly explain how their appraoch is distinct from previous approaches. - The figures effectively describe the concepts discussed. Weaknesses and Questions. - In the introduction, the authors mention one-mode projection. Is this equivalent to clique expansion? >> Thank you for the clarification. We have added the response as the following sentences in the discussion. "In this study, we used the clique expansion technique, which is equivalent to the muscle-centric one-mode projection of the bipartite network of muscles and bones. Since the clique expansion forms a complete graph connecting all nodes within the same hyperedge, it is equivalent to the one-mode projection connecting all nodes that have a common neighbor, i.e., a hyperedge." - The datasets used in the paper are too small. Please consider using larger datasets if possible. >> This study aims to compare the representation performance of different embedding methods on small datasets of major muscles and bones. Therefore, we believe that the application of larger datasets is beyond the scope of this study. In the future analysis, we plan to use larger datasets, which are described in the revised sentences below: "We used only a limited part of the anatomical network data in this study and will include more detailed and multiple types of datasets that can be represented by multilayer network models\\cite{de2023more}. As we have shown in the Supporting information \\nameref{S1_Table}, NAnaTex has more and larger datasets that reflect the complex interconnectivity of the body, which we plan to use for the future study." - Only a small number of embedding methods are employed. Other graph-based methods such as node2vec and hypergraph-based methods such as hyper2vec or VilLain could also be considered. Are the results consistent when using these additional methods? Please refer to the references below. References. - node2vec: Scalable feature learning for networks (KDD 2016) - Hyper2vec: Biased random walk for hyper-network embedding (DSAFAA workshop 2019) - VilLain: Self-Supervised Learning on Homogeneous Hypergraphs without Features via Virtual Label Propagation (WWW 2024) >> Thank you for the valuable information. Following your suggestion, we employed five additional embedding methods, including node2vec, to compare their performance. The main differences between the 8 methods, such as network type (pairwise / hypergraph), embedding (proximity / structure), and learning (supervised / unsupervised) criteria, are shown in Table 3. Due to time constraints,　we added the embedding methods we were familiar with in this revision. We employed HGNNP and UniSAGE, instead of hyper2vec and VilLain, which are also hypergraph-based, but learn proximity and structural features, respectively. - The embedding methods discussed can also be applied in star expansion. How does that impact the results? >> Thank you for your insightful comment. We have added the response as the following sentences in the discussion. "We chose the clique expansion (one-mode projection) over the star expansion because it is more suitable for applications that focus on the relationships between muscles. In star expansion, a new central node is introduced for each hyperedge that connects to the associated muscles, which is equivalent to reverting to the original bipartite network of muscles and bones. This can obscure the direct relationships between muscles by shifting some of the focus to the central nodes (bones), thus detracting from a muscle-centric analysis." - While the analysis of the results using the embedding methods is the main contribution of the paper, the discussion of the methods themselves is overly detailed. I recommend simplifying the description of the methods and focusing more on the experimental resutls and analyses. >> Following the reviewer's recommendation, we have revised the method to describe it more simply. - For better readability, please structure the discussion more clearly. I recommend the authors revise the manuscript to enhance clarity and focus. >> Thank you for the useful suggestion. According to the reviewer's suggestion, we have carefully read the entire structure of the discussion and revised the sentences. Attachments Attachment Submitted filename: response_v3f.txt https://doi.org/10.1371/journal.pone.0310189.r002
27 Aug 2024 Decision Letter - Hocine Cherifi, Editor Hypergraph modeling of complex interactions: Applications from human musculoskeletal structures to complex system dynamics PONE-D-24-20578R1 Dear Dr. yamano, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, 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, Hocine Cherifi Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0310189.r003
Formally Accepted
10 Sep 2024 Acceptance Letter - Hocine Cherifi, Editor PONE-D-24-20578R1 PLOS ONE Dear Dr. Yamano, 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 Professor Hocine Cherifi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0310189.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .