ArticleBMC bioinformatics2019
A deep learning method to more accurately recall known lysine acetylation sites.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Beyond the Capsid: How Can Post-Translational Modifications Modulate the Multifunctionality of the Orthoflavivirus Capsid Protein?Molecules (Basel, Switzerland) · 2026Review
- Integrative pan-cancer and COAD-focused multi-omics analysis of non-histone acetylation substrate-related transcriptional landscapes.NPJ precision oncology · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Learning the PTM code through a coarse-to-fine mechanism-aware framework.Nature communications · 2026Article
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- Post-translational modifications in the Protein Data Bank.Acta crystallographica. Section D, Structural biology · 2024Review
- TransPTM: a transformer-based model for non-histone acetylation site prediction.Briefings in bioinformatics · 2024Article
- A deep neural network-based approach for seizure activity recognition of epilepsy sufferers.Frontiers in medicine · 2024Article
- Predicting cysteine reactivity changes upon phosphorylation using XGBoost.FEBS open bio · 2024Article
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Mini-review: Recent advances in post-translational modification site prediction based on deep learning.Computational and structural biotechnology journal · 2022Review
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site forFrontiers in bioinformatics · 2022Article
- MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.Biomolecules · 2021Article
- UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites.Genes · 2021Article
Corrections and comments
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4 authors.
Funding
Abstract
backgroundLysine acetylation in protein is one of the most important post-translational modifications (PTMs). It plays an important role in essential biological processes and is related to various diseases. To obtain a comprehensive understanding of regulatory mechanism of lysine acetylation, the key is to identify lysine acetylation sites. Previously, several shallow machine learning algorithms had been applied to predict lysine modification sites in proteins. However, shallow machine learning has some disadvantages. For instance, it is not as effective as deep learning for processing big data.
resultsIn this work, a novel predictor named DeepAcet was developed to predict acetylation sites. Six encoding schemes were adopted, including a one-hot, BLOSUM62 matrix, a composition of K-space amino acid pairs, information gain, physicochemical properties, and a position specific scoring matrix to represent the modified residues. A multilayer perceptron (MLP) was utilized to construct a model to predict lysine acetylation sites in proteins with many different features. We also integrated all features and implemented the feature selection method to select a feature set that contained 2199 features. As a result, the best prediction achieved 84.95% accuracy, 83.45% specificity, 86.44% sensitivity, 0.8540 AUC, and 0.6993 MCC in a 10-fold cross-validation. For an independent test set, the prediction achieved 84.87% accuracy, 83.46% specificity, 86.28% sensitivity, 0.8407 AUC, and 0.6977 MCC.
conclusionThe predictive performance of our DeepAcet is better than that of other existing methods. DeepAcet can be freely downloaded from https://github.com/Sunmile/DeepAcet .
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