Incorporation of Kernel Support Vector Machine for Effective Prediction of Lysine Formylation from Class Imbalance Samples
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-6636-0_15
Reference28 articles.
1. Yu B, Yu Z, Chen C et al (2020) DNNAce: prediction of prokaryote lysine acetylation sites through deep neural networks with multi-information fusion. Chemom Intell Lab Syst 200(5):103999–104014. https://doi.org/10.1016/j.chemolab.2020.103999
2. Ning Q, Ma Z, Zhao X (2019) dForml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components. J Theor Bio 470(7):43–49. https://doi.org/10.1016/j.jtbi.2019.03.011
3. Ju Z, Wang S (2020) Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou’s 5-steps rule and general pseudo components. Genomics 112(1):859–866. https://doi.org/10.1016/j.ygeno.2019.05.027
4. Jia C, Zhang M, Fan C et al (2019) Formator: predicting lysine formylation sites based on the most distant undersampling and safe-level synthetic minority oversampling. IEEE/ACM Trans Computat Biol Bioinf. https://doi.org/10.1109/tcbb.2019.2957758
5. Jiang T, Zhou X, Taghizadeh K et al (2006) N-formylation of lysine in histone proteins as a secondary modification arising from oxidative DNA damage. Proc Nat Acad Sci 104(1):60–65. https://doi.org/10.1073/pnas.0606775103
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