ArticleBMC bioinformatics2014
Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.Scientific reports · 2024Article
- Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots inFrontiers in genetics · 2021Article
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding.International journal of molecular sciences · 2019Article
- Prediction of Protein-Protein Interactions Based on Domain.Computational and mathematical methods in medicine · 2019Article
- Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.BMC bioinformatics · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation.Combinatorial chemistry & high throughput screening · 2018Article
- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
- Recombination spot identification Based on gapped k-mers.Scientific reports · 2016Article
- Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.
resultsHere we proposed a novel approach by fusing features from pseudo nucleic acid composition (PseNAC), including NAC, n-tier NAC and pseudo dinucleotide composition (PseDNC). A recursive feature extraction by linear kernel support vector machine (SVM) was then used to rank the integrated feature vectors and extract optimal features. SVM was adopted for identifying recombination spots based on these optimal features. To evaluate the performance of the proposed method, jackknife cross-validation test was employed on a benchmark dataset. The overall accuracy of this approach was 84.09%, which was higher (from 0.37% to 3.79%) than those of state-of-the-art tools.
conclusionsComparison results suggested that linear kernel SVM is a useful vehicle for identifying recombination hot/cold spots.
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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.