ArticleFrontiers in genetics2018
Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications.
Article in Frontiers in genetics, 2018. 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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Who cites it
10 citing papers in PubMed, 22 citations in OpenAlex.
- Adverse maternal environment affects hippocampal HTR2c variant expression and epigenetic characteristics in mouse offspring.Pediatric research · 2022Article
- Role of Epitranscriptomic and Epigenetic Modifications during the Lytic and Latent Phases of Herpesvirus Infections.Microorganisms · 2022Review
- The SWI/SNF subunit BRG1 affects alternative splicing by changing RNA binding factor interactions with nascent RNA.Molecular genetics and genomics : MGG · 2022Article
- iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.Computational and mathematical methods in medicine · 2021Article
- A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol.Frontiers in bioengineering and biotechnology · 2020Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Disease modeling of core pre-mRNA splicing factor haploinsufficiency.Human molecular genetics · 2019Article
- Combinatorial Pattern of Histone Modifications in Exon Skipping Event.Frontiers in genetics · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative splicing (AS) not only ensures the diversity of gene expression products, but also closely correlated with genetic diseases. Therefore, knowledge about regulatory mechanisms of AS will provide useful clues for understanding its biological functions. In the current study, a random forest based method was developed to classify included and excluded exons in exon skipping event. In this method, the samples in the dataset were encoded by using optimal histone modification features which were optimized by using the Maximum Relevance Maximum Distance (MRMD) feature selection technique. The proposed method obtained an accuracy of 72.91% in 10-fold cross validation test and outperformed existing methods. Meanwhile, we also systematically analyzed the distribution of histone modifications between included and excluded exons and discovered their preference in both kinds of exons, which might provide insights into researches on the regulatory mechanisms of alternative splicing.
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