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As you can see from the reviewer comments, all their concerns have been addressed. The only minor comment is regarding the typos and grammatical mistakes that require still a proof-reading.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
**PeerJ Staff Note:** Although the Academic and Section Editors are happy to accept your article as being scientifically sound, a final check of the manuscript shows that it would benefit from further English editing. Therefore, please identify necessary edits and address these while in proof stage.
In the paper, biological sequences are
represented with time series to obtain biological time sequence (BTS).
Experiments are executed to evaluate the performance of the proposed scheme.
Experiment results show the effectiveness of the proposed scheme.
There are some typos and grammatical mistakes. The authors must proofread the entire paper.
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Please find the reviewer reports attached, both reviewers see the potential in the paper but also raise major concerns regarding its current state. Personally, I agree that the current form is not very approachable and that further clarifications are needed for a better and easier understanding of the approach.
[# PeerJ Staff Note: Please ensure that all review and editorial comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. #]
[# PeerJ Staff Note: PeerJ staff have identified that the English language needs to be improved. When you prepare your next revision, please either (i) have a colleague who is proficient in English and familiar with the subject matter review your manuscript, or (ii) contact a professional editing service to review your manuscript. PeerJ can provide language editing services - you can contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title) #]
In the paper, biological sequence is represented with time series to form biological time sequence (BTS).
In the DNA sequence experiments of six kinds of viruses, SaPt-CNN-LSTM-AR-EA realized good overall prediction performance, the prediction accuracy and correlation were 1.7073 and 0.9186, respectively.
The effectiveness and stability of SaPt-CNN-LSTM-AR-EA were verified through the comparison with other ûve benchmark models. In addition, compared with other benchmark models, SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%.
1. The Abstract should be very precise.
2. Motivations of the paper are not clear.
3. Contributions are not specific. The last paragraph of the Introduction should be the structure of the paper. Most importantly, the structure of the Introduction section is very poor.
3. Related schemes are not discussed properly. The following recent papers must be cited to improve related schemes:
a) Probabilistic time series forecasting with deep non-linear state space models
b) Research on face intelligent perception technology integrating deep learning under different illumination intensities
c) Enhanced neural network-based univariate time-series forecasting model for big data
d) Research on trend prediction of component stock in fuzzy time series based on deep forest
e) Enhancing randomness of the ciphertext generated by DNA-based cryptosystem and finite state machine
f) Coupling adversarial learning with selective voting strategy for distribution alignment in partial domain adaptation
g) Ensemble algorithm using transfer learning for sheep breed classification
4. Algorithms of the proposed scheme are not mentioned properly.
5. Why and how DNA computing is used in this paper.
6. The proposed scheme is unstructured. It is hard to identify the novelty of the proposed work.
7. Equations and figures are not represented properly. Use an appropriate software to draw the figures of the result section.
8. Technical discussion on results is not given. Moreover, the results are not convincing.
9. The English language is very poor.
10. The organization of the paper is poor.
11. Important references are missing and all the details of the references are not given.
12. For existing papers, the surname of the authors must be mentioned. Not full author's name.
This paper proposed a hybrid ensemble learning framework for biological time sequence.
1. The contributions and novelty of this study are not clear, compared with the authors' previous study and existing literature. The proposed framework seems to be a simple combination of existing deep learning modules.
2. The mathematical symbols and equations in this manuscript have a terrible format and should be improved.
3. The figures have a low quality and should be improved with a higher resolution.
1. The experiments are not enough. The ablation study is missing in this manuscript.
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