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- W2399634880 abstract "Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor." @default.
- W2399634880 created "2016-06-24" @default.
- W2399634880 creator A5019802421 @default.
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- W2399634880 creator A5045599932 @default.
- W2399634880 creator A5070373092 @default.
- W2399634880 creator A5072443515 @default.
- W2399634880 date "2016-05-16" @default.
- W2399634880 modified "2023-09-27" @default.
- W2399634880 title "Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set" @default.
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- W2399634880 doi "https://doi.org/10.1371/journal.pone.0155370" @default.
- W2399634880 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4868276" @default.
- W2399634880 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27183223" @default.
- W2399634880 hasPublicationYear "2016" @default.
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