ArticleInternational journal of biological sciences2018
iRSpot-Pse6NC: Identifying recombination spots in
Article in International journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 144 citations in OpenAlex.
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- Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.International journal of molecular sciences · 2022Article
- M6A-BiNP: predicting NRNA biology · 2021Article
- Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features.BMC bioinformatics · 2021Article
- Article
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites.Current genomics · 2020Review
- iterb-PPse: Identification of transcriptional terminators in bacterial by incorporating nucleotide properties into PseKNC.PloS one · 2020Article
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.Molecular therapy. Nucleic acids · 2019Article
- iRNA-m7G: Identifying NMolecular therapy. Nucleic acids · 2019Article
- Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.Briefings in bioinformatics · 2019Review
- iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features.Molecular genetics and genomics : MGG · 2019Article
- Identification of D Modification Sites by Integrating Heterogeneous Features inMolecules (Basel, Switzerland) · 2019Article
- Application of Machine Learning in Microbiology.Frontiers in microbiology · 2019Review
- Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs.Medicinal chemistry (Shariqah (United Arab Emirates)) · 2019Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meiotic recombination caused by meiotic double-strand DNA breaks. In some regions the frequency of DNA recombination is relatively higher, while in other regions the frequency is lower: the former is usually called "recombination hotspot", while the latter the "recombination coldspot". Information of the hot and cold spots may provide important clues for understanding the mechanism of genome revolution. Therefore, it is important to accurately predict these spots. In this study, we rebuilt the benchmark dataset by unifying its samples with a same length (131 bp). Based on such a foundation and using SVM (Support Vector Machine) classifier, a new predictor called "iRSpot-Pse6NC" was developed by incorporating the key hexamer features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. It has been observed via rigorous cross-validations that the proposed predictor is superior to its counterparts in overall accuracy, stability, sensitivity and specificity. For the convenience of most experimental scientists, the web-server for iRSpot-Pse6NC has been established at http://lin-group.cn/server/iRSpot-Pse6NC, by which users can easily obtain their desired result without the need to go through the detailed mathematical equations involved.
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Registered trials
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.