All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Reviewers are satisfied with the revisions, and I concur to recommend accepting this manuscript.
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a 'PeerJ Computer Science' Section Editor covering this Section #]
No further comment. Thank you for your kind responses, and I am happy that we can now have almost the same understanding of your research.
No further comment.
No further comment.
The quality of this article meets the journal's standard.
The experimental design of this article meets the journal's standard.
The findings have been properly validated.
The reviewers have substantial concerns about this manuscript. The authors should provide point-to-point responses to address all the concerns and provide a revised manuscript with the revised parts being marked in different color.
The authors construct a comprehensive computational model, MFPIC, to predict protein-protein interactions by integrating diverse sequence-based features. Although experimental techniques for PPI detection have advanced, their limitations—such as high false-positive rates and resource intensity—necessitate robust computational approaches. However, I have several major concerns regarding the results and explanations provided, as outlined below:
1. The introduction of SCT and AAPD as novel features is commendable. However, further justification or real-world examples highlighting their unique contributions compared to traditional methods like PSSM is needed for better context.
2. While the manuscript benchmarks MFPIC against existing models, it would benefit from a discussion on why certain benchmarks (e.g., LightGBM-PPI, GTB-PPI) perform differently and what specific elements of MFPIC lead to its superior performance.
3. While metrics like accuracy, precision, and MCC are reported, a deeper analysis of false positives and false negatives could provide insights into the model's limitations.
1. The manuscript mentions cross-validation but lacks specifics about the splits (e.g., ratio, fold count). Providing these details ensures transparency in model evaluation and reproducibility.
1. The manuscript lacks a thorough discussion of the limitations of the proposed model.
2. Although the manuscript mentions that the source code and datasets are available on GitHub, there is no clear documentation or well-structured code provided.
1. The authors thoroughly review the relevant work on PPI prediction. It is recommended that the authors provide a more detailed classification of these methods and appropriately evaluate them to highlight the limitations of existing approaches.
2. The authors use multiple benchmark datasets to evaluate the proposed model. It is suggested that the authors include a table displaying the details of these datasets.
3. The flowchart of the model is not sufficiently clear. It is recommended that the authors add more descriptive content in the figure caption to improve the manuscript’s readability.
1. The manuscript compares the proposed model with different state-of-the-art methods on various datasets. However, the proposed model is a general approach for PPI prediction, it would be more appropriate to compare its performance with the same state-of-the-art methods across all benchmark datasets.
1. The authors construct protein features using various properties and encoding schemes. However, the contribution of each encoding scheme is unclear. The authors should include experimental results to demonstrate the contribution of different encoding methods.
2. An essential ablation analysis is lacking. The authors should indicate the contribution of different experimental settings or components to the prediction results.
1. Some recently published papers dedicated to the prediction of PPIs were missed in related work. A more comprehensive literature review should be presented.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.