ArticlePloS one2019
iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites.Journal of computer-aided molecular design · 2026Article
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- [Prediction of protein Kbhb sites based on learnable feature embedding].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- 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
- Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins.Frontiers in cellular and infection microbiology · 2025Article
- Review
- PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants.Plant methods · 2024Article
- CapsNh-Kcr: Capsule network-based prediction of lysine crotonylation sites in human non-histone proteins.Computational and structural biotechnology journal · 2023Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 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
- m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.Bioinformatics (Oxford, England) · 2022Article
- Identification of stress response proteins through fusion of machine learning models and statistical paradigms.Scientific reports · 2021Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- Protein lysine crotonylation: past, present, perspective.Cell death & disease · 2021Review
- Article
- A Transfer Learning-Based Approach for Lysine Propionylation Prediction.Frontiers in physiology · 2021Article
- Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Among different post-translational modifications (PTMs), one of the most important one is the lysine crotonylation in proteins. Its importance cannot be undermined related to different diseases and essential biological practice. The key step for finding the hidden mechanisms of crotonylation along with their occurrence sites is to completely apprehend the mechanism behind this biological process. In previously reported studies, researchers have used different techniques, like position weighted matrix (PWM), support vector machine (SVM), k nearest neighbors (KNN), and many others. However, the maximum prediction accuracy achieved was not such high. To address this, herein, we propose an improved predictor for lysine crotonylation sites named iCrotoK-PseAAC, in which we have incorporated various position and composition relative features along with statistical moments into PseAAC. The results of self-consistency testing were 100% accurate, while the 10-fold cross validation gave 99.0% accuracy. Based on the validation and comparison of model, it is concluded that the iCrotoK-PseAAC is more accurate than the previously proposed models.
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