ArticleMolecular genetics and genomics : MGG2016
iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.
Article in Molecular genetics and genomics : MGG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 133 citations in OpenAlex.
- iAcety-SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest.Membranes · 2022Article
- Identification of intelligence-related proteins through a robust two-layer predictor.Communicative & integrative biology · 2022Article
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach.Frontiers in genetics · 2020Article
- Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature ExtractionCurrent genomics · 2019Article
- MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.Bioinformatics (Oxford, England) · 2019Article
- iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- Article
- RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.Database : the journal of biological databases and curation · 2019Article
- PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.Scientific reports · 2018Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- iRNA-3typeA: Identifying Three Types of Modification at RNA's Adenosine Sites.Molecular therapy. Nucleic acids · 2018Article
- 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.BMC systems biology · 2018Article
- iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Oncotarget · 2017Article
- iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.Oncotarget · 2017Article
- 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.Molecular therapy. Nucleic acids · 2017Article
- iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.Molecular therapy. Nucleic acids · 2017Article
- Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.Oncotarget · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meiotic recombination is vital for maintaining the sequence diversity in human genome. Meiosis and recombination are considered the essential phases of cell division. In meiosis, the genome is divided into equal parts for sexual reproduction whereas in recombination, the diverse genomes are combined to form new combination of genetic variations. Recombination process does not occur randomly across the genomes, it targets specific areas called recombination "hotspots" and "coldspots". Owing to huge exploration of polygenetic sequences in data banks, it is impossible to recognize the sequences through conventional methods. Looking at the significance of recombination spots, it is indispensable to develop an accurate, fast, robust, and high-throughput automated computational model. In this model, the numerical descriptors are extracted using two sequence representation schemes namely: dinucleotide composition and trinucleotide composition. The performances of seven classification algorithms were investigated. Finally, the predicted outcomes of individual classifiers are fused to form ensemble classification, which is formed through majority voting and genetic algorithm (GA). The performance of GA-based ensemble model is quite promising compared to individual classifiers and majority voting-based ensemble model. iRSpot-GAEnsC has achieved 84.46 % accuracy. The empirical results revealed that the performance of iRSpot-GAEnsC is not only higher than the examined algorithms but also better than existing methods in the literature developed so far. It is anticipated that the proposed model might be helpful for research community, academia and for drug discovery.
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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.