ArticleScientific reports2016
Recombination spot identification Based on gapped k-mers.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 30 citations in OpenAlex.
- Krein support vector machine classification of antimicrobial peptides.Digital discovery · 2023Article
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- An efficient strategy usingHeliyon · 2017Article
- Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.BMC systems biology · 2016Article
- iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance.Scientific reports · 2016Article
- Identification of apolipoprotein using feature selection technique.Scientific reports · 2016Article
- Optimization to the Culture Conditions for Phellinus Production with Regression Analysis and Gene-Set Based Genetic Algorithm.BioMed research international · 2016Article
- Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition.BioMed research international · 2016Article
- BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.BioMed research international · 2016Article
- Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.BioMed research international · 2016Article
- DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.PloS one · 2016Article
- Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition.BioMed research international · 2016Article
- Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.International journal of genomics · 2016Article
- Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest.Scientifica · 2016Article
- In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.BioMed research international · 2016Article
- A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network.BioMed research international · 2016Article
- Identification of Secretory Proteins in Mycobacterium tuberculosis Using Pseudo Amino Acid Composition.BioMed research international · 2016Article
Corrections and comments
- Correction · 2016-12-07Error in Text · Euphemisms for Plagiarism · Plagiarism of/in Article ·
- Retraction · 2018-03-20Euphemisms for Plagiarism · Objections by Author(s) · Plagiarism of/in Article ·
- Erratum issued
- Retracted
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have proposed several computational methods for recombination spot identification. The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics.
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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.