ArticleOncotarget2018
DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro.Scientific reports · 2021Article
- SAResNet: self-attention residual network for predicting DNA-protein binding.Briefings in bioinformatics · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.Plant molecular biology · 2020Article
- A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods.Current genomics · 2020Review
- Extremely-randomized-tree-based Prediction of NCurrent genomics · 2020Article
- i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes.Computational and structural biotechnology journal · 2020Article
- Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.Frontiers in bioengineering and biotechnology · 2020Article
- Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- Article
- iProEP: A Computational Predictor for Predicting Promoter.Molecular therapy. Nucleic acids · 2019Article
- Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding.International journal of molecular sciences · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
- Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.International journal of molecular sciences · 2019Article
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3 authors.
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Abstract
DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.
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