Computational Approaches for Peroxisomal Protein Localization
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Publisher
Springer US
Link
https://link.springer.com/content/pdf/10.1007/978-1-0716-3048-8_29
Reference26 articles.
1. Anteghini M, Martins dos Santos VAP, Saccenti E (2021) In-Pero: exploiting deep learning Embeddings of protein sequences to predict the localisation of Peroxisomal proteins. Int J Mol Sci 22(12):6409
2. Savojardo C, Bruciaferri N, Tartari G et al (2019) DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks. Bioinformatics 36(1):56–64
3. Schlüter A, Real-Chicharro A, Gabaldón T et al (2009) PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome. Nuc Acid Res 38:D800–D805
4. Claros MG, Vincens P (1996) Computational method to predict Mitochondrially imported proteins and their targeting sequences. Eur J Biochem 241(3):779–786
5. Anteghini M, Haja A, Martins dos Santos VAP et al (2022) OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localisation. bioRxiv. https://doi.org/10.1101/2022.06.21.497045
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