ArticlePloS one2014
LAceP: lysine acetylation site prediction using logistic regression classifiers.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed, 122 citations in OpenAlex.
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences.Briefings in bioinformatics · 2023Article
- iAcety-SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest.Membranes · 2022Article
- STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.Briefings in bioinformatics · 2022Article
- Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Machine learning classification of trajectories from molecular dynamics simulations of chromosome segregation.PloS one · 2022Article
- Mini-review: Recent advances in post-translational modification site prediction based on deep learning.Computational and structural biotechnology journal · 2022Review
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- Article
- PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids.Genes · 2020Article
- Protein acetylation and deacetylation: An important regulatory modification in gene transcription (Review).Experimental and therapeutic medicine · 2020Review
- Bromodomain and extra-terminal domain (BET) proteins regulate melanocyte differentiation.Epigenetics & chromatin · 2020Article
- Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.BMC molecular and cell biology · 2019Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
- LMO2 activation by deacetylation is indispensable for hematopoiesis and T-ALL leukemogenesis.Blood · 2019Article
- Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.BMC bioinformatics · 2019Article
- EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction.BMC genomics · 2019Article
- A deep learning method to more accurately recall known lysine acetylation sites.BMC bioinformatics · 2019Article
- Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation.Frontiers in bioengineering and biotechnology · 2019Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding.
resultIn this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.
conclusionLAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.