ArticleScientific reports2016
Mal-Lys: prediction of lysine malonylation sites in proteins integrated sequence-based features with mRMR feature selection.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- Minimum Redundancy Maximum Relevance Feature Selection-Application on Single-Cell RNA Sequencing Dataset.Advances in experimental medicine and biology · 2026Article
- Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.Database : the journal of biological databases and curation · 2024Review
- SIRT5-related lysine demalonylation of GSTP1 contributes to cardiomyocyte pyroptosis suppression in diabetic cardiomyopathy.International journal of biological sciences · 2024Article
- A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.Genomics, proteomics & bioinformatics · 2023Review
- PreAcrs: a machine learning framework for identifying anti-CRISPR proteins.BMC bioinformatics · 2022Article
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
- BERT-Kgly: A Bidirectional Encoder Representations From Transformers (BERT)-Based Model for Predicting Lysine Glycation Site forFrontiers in bioinformatics · 2022Article
- Malonate utilization by Pseudomonas aeruginosa affects quorum-sensing and virulence and leads to formation of mineralized biofilm-like structures.Molecular microbiology · 2021Article
- Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.BMC bioinformatics · 2021Article
- Article
- Mal-Prec: computational prediction of protein Malonylation sites via machine learning based feature integration : Malonylation site prediction.BMC genomics · 2020Article
- Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma.Nature communications · 2020Article
- Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.IEEE access : practical innovations, open solutions · 2020Article
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- Predicting Cell Wall Lytic Enzymes Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
- Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.BMC molecular and cell biology · 2019Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.Briefings in bioinformatics · 2019Article
- Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.Briefings in bioinformatics · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lysine malonylation is an important post-translational modification (PTM) in proteins, and has been characterized to be associated with diseases. However, identifying malonyllysine sites still remains to be a great challenge due to the labor-intensive and time-consuming experiments. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor Mal-Lys which incorporated residue sequence order information, position-specific amino acid propensity and physicochemical properties was proposed. A feature selection method of minimum Redundancy Maximum Relevance (mRMR) was used to select optimal ones from the whole features. With the leave-one-out validation, the value of the area under the curve (AUC) was calculated as 0.8143, whereas 6-, 8- and 10-fold cross-validations had similar AUC values which showed the robustness of the predictor Mal-Lys. The predictor also showed satisfying performance in the experimental data from the UniProt database. Meanwhile, a user-friendly web-server for Mal-Lys is accessible at http://app.aporc.org/Mal-Lys/.
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Registered trials
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