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• W3199970975 abstract "Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION." @default.
• W3199970975 created "2021-09-27" @default.
• W3199970975 creator A5040180866 @default.
• W3199970975 creator A5055306211 @default.
• W3199970975 creator A5065292282 @default.
• W3199970975 date "2021-09-17" @default.
• W3199970975 modified "2023-10-14" @default.
• W3199970975 title "STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction" @default.
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• W3199970975 doi "https://doi.org/10.1093/bib/bbab376" @default.
• W3199970975 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34532736" @default.
• W3199970975 hasPublicationYear "2021" @default.
• W3199970975 type Work @default.
• W3199970975 sameAs 3199970975 @default.

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