TAPE-Pero: Using Deep Representation Learning Model to Identify and Localize Peroxisomal Proteins
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4749-2_48
Reference20 articles.
1. Zhou, H., Chen, C., Wang, M., Ma, Q., Yu, B.: Predicting golgi-resident protein types using conditional covariance minimization with XGBoost based on multiple features fusion. IEEE Access 7, 144154–144164 (2019)
2. Lv, Z., Jin, S., Ding, H., Zou, Q.: A random forest sub-Golgi protein classifier optimized via dipeptide and amino acid composition features. Front. Bioeng. Biotechnol. 7, 215 (2019)
3. Yu, B., et al.: SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 36(4), 1074–1081 (2020)
4. Ahmad, J., Hayat, M.: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J. Theor. Biol. 463, 99–109 (2019)
5. Qiu, W., et al.: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition. J. Theor. Biol. 450, 86–103 (2018)
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