ArticleScientific reports2022
A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 20 citations in OpenAlex.
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- Epigenome and interactome profiling uncovers principles of distal regulation in the barley genome.Cell genomics · 2026Article
- Microglial Epigenetic Memory is Associated with Accelerated Resolution of Inflammatory Pain Induced by Prophylactic Macrophage-Derived Small Extracellular Vesicles.Journal of inflammation research · 2026Article
- An ensemble strategy for piRNA identification through hybrid moment-based feature modeling.Scientific reports · 2025Article
- Gene replacement therapies for inherited disorders of neurotransmission: Current progress in succinic semialdehyde dehydrogenase deficiency.Journal of inherited metabolic disease · 2024Review
- m1A-Ensem: accurate identification of 1-methyladenosine sites through ensemble models.BioData mining · 2024Article
- Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique.Frontiers in cellular and infection microbiology · 2023Article
- m5c-iEnsem: 5-methylcytosine sites identification through ensemble models.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. Given their distance from the target gene, lack of common motifs, and tissue/cell specificity, enhancer regions are thought to be difficult to predict in DNA sequences. Recently, a number of bioinformatics tools were created to distinguish enhancers from other regulatory components and to pinpoint their advantages. However, because the quality of its prediction method needs to be improved, its practical application value must also be improved. Based on nucleotide composition and statistical moment-based features, the current study suggests a novel method for identifying enhancers and non-enhancers and evaluating their strength. The proposed study outperformed state-of-the-art techniques using fivefold and tenfold cross-validation in terms of accuracy. The accuracy from the current study results in 86.5% and 72.3% in enhancer site and its strength prediction respectively. The results of the suggested methodology point to the potential for more efficient and successful outcomes when statistical moment-based features are used. The current study's source code is available to the research community at https://github.com/csbioinfopk/enpred .
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