Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling-Reference-Cited by-同舟云学术

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Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling

Published:2023 Issue: Volume: Page:23-39
ISSN:2364-6748
Container-title:Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling
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Author:

Pandey Rakesh Kumar,Tiwari Anoop Kumar,Shreevastava Shivam,Som Tanmoy

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8566-9_2

Reference46 articles.

1. Xu, D., et al.: ACHP: A web server for predicting anti-cancer peptide and anti-hypertensive peptide. Int. J. Peptide Res. Ther. 1–12 (2021)

2. Lane, N., Kahanda, I.: DeepACPpred: a novel hybrid CNN-RNN architecture for predicting anti-cancer peptides. In: International Conference on Practical Applications of Computational Biology & Bioinformatics. Springer, Cham (2020)

3. Sakamoto, K., Masutani, T., Hirokawa, T.: Generation of KS-58 as the first K-Ras (G12D)-inhibitory peptide presenting anti-cancer activity in vivo. Sci. Rep. 10(1), 1–16 (2020)

4. Wei, L., et al.: ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 34(23), 4007–4016 (2018)

5. You, H., et al.: Anti-cancer peptide recognition based on grouped sequence and spatial dimension integrated networks. Interdiscipl. Sci.: Comput. Life Sci. 1–13 (2021)

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