ArticleScientific reports2020
Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MVFAN-Kcr: A Multi-View Feature Fusion and Attention-Based Network for Lysine Crotonylation Site Identification.Interdisciplinary sciences, computational life sciences · 2026Article
- A deep learning model for prediction of lysine crotonylation sites by fusing multi-features based on multi-head self-attention mechanism.Scientific reports · 2025Article
- iKcr-DRC: prediction of lysine crotonylation sites in proteins based on a novel attention module and DenseNet.Frontiers in genetics · 2025Article
- TCellPredX: A Novel Approach for Accurate Prediction of Hepatitis C Virus Linear T Cell Epitopes.ACS omega · 2024Article
- Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species.International journal of molecular sciences · 2024Article
- KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.Genomics, proteomics & bioinformatics · 2023Article
- CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins.Computational and structural biotechnology journal · 2023Article
- On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Current status of PTMs structural databases: applications, limitations and prospects.Amino acids · 2022Review
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.Briefings in bioinformatics · 2022Article
- iKcr_CNN: A novel computational tool for imbalance classification of human nonhistone crotonylation sites based on convolutional neural networks with focal loss.Computational and structural biotechnology journal · 2022Article
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5 authors.
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Abstract
Lysine crotonylation (Kcr) is a type of protein post-translational modification (PTM), which plays important roles in a variety of cellular regulation and processes. Several methods have been proposed for the identification of crotonylation. However, most of these methods can predict efficiently only on histone or non-histone protein. Therefore, this work aims to give a more balanced performance in different species, here plant (non-histone) and mammalian (histone) are involved. SVM (support vector machine) and RF (random forest) were employed in this study. According to the results of cross-validations, the RF classifier based on EGAAC attribute achieved the best predictive performance which performs competitively good as existed methods, meanwhile more robust when dealing with imbalanced datasets. Moreover, an independent test was carried out, which compared the performance of this study and existed methods based on the same features or the same classifier. The classifiers of SVM and RF could achieve best performances with 92% sensitivity, 88% specificity, 90% accuracy, and an MCC of 0.80 in the mammalian dataset, and 77% sensitivity, 83% specificity, 70% accuracy and 0.54 MCC in a relatively small dataset of mammalian and a large-scaled plant dataset respectively. Moreover, a cross-species independent testing was also carried out in this study, which has proved the species diversity in plant and mammalian.
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