ArticleIEEE access : practical innovations, open solutions2020
Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Enhancing the identification of malonylation sites using AlphaFold2 and ensemble learning.Molecular diversity · 2026Article
- KmalPred: a deep learning framework for lysine malonylation site prediction using protein language model representations.BMC biology · 2026Article
- BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.BMC genomics · 2026Article
- An efficient machine-learning framework for predicting protein post-translational modification sites.Scientific reports · 2025Article
- CNN-Meth: A Tool to Accurately Predict Lysine Methylation Sites Using Evolutionary Information-Based Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2025Article
- 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
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
- Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.Genes · 2020Article
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Authors and funding
7 authors at 4 institutions in 6 countries.
Funding
Abstract
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
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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.