ArticleBriefings in bioinformatics2022
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 80 citations in OpenAlex.
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- BEX4 inhibits the progression of clear cell renal cell carcinoma by stabilizing SH2D4A, which is achieved by blocking SIRT2 activity.Oncogene · 2025Article
- Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A two-task predictor for discovering phase separation proteins and their undergoing mechanism.Briefings in bioinformatics · 2024Article
- StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.BMC bioinformatics · 2024Article
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- DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding.Frontiers in genetics · 2024Article
- Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.Briefings in bioinformatics · 2023Article
- MSTL-Kace: Prediction of Prokaryotic Lysine Acetylation Sites Based on Multistage Transfer Learning Strategy.ACS omega · 2023Article
- ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.Briefings in bioinformatics · 2023Article
- PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.Bioinformatics (Oxford, England) · 2023Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Bitter-RF: A random forest machine model for recognizing bitter peptides.Frontiers in medicine · 2023Article
- An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation.Research (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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