Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier.
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Summary
The proposed approach outperformed similarity- based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets.
- Type
- book
- Published
- 2016-11-05
- Cited by
- 22
- References
- 25
- OpenAlex
- https://openalex.org/W27393648
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:26993438
Keywords
Environmental science
References
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- Use of DNA barcodes to identify flowering plants.
- A Two-Locus Global DNA Barcode for Land Plants: The Coding rbcL Gene Complements the Non-Coding trnH-psbA Spacer Region
- ANCHOR: web server for predicting protein binding regions in disordered proteins
- A proposal for a standardised protocol to barcode all land plants
- Supervised DNA Barcodes species classification: analysis, comparisons and results
- Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: the tortoise and the hare III.
- The effect of nonsense codons on splicing: a genomic analysis.
Cited by
- Supervised SVM Classification of Rainfall Datasets
- The transcription factor Batf3 inhibits the differentiation of regulatory T cells in the periphery
- A Data Adaptive Biological Sequence Representation for Supervised Learning
- HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine
- funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model
- New Intraclass Helitrons Classification Using DNA-Image Sequences and Machine Learning Approaches
- Methylation-driven model for analysis of dinucleotide evolution in genomes
- Evaluating the number of different genomes in a metagenome by means of the compositional spectra approach
- Efficacy and accuracy responses of DNA mini-barcodes in species identification under a supervised machine learning approach
- Identification of SARS-CoV-2 origin: Using Ngrams, principal component analysis and Random Forest algorithm
- ASLncR: a novel computational tool for prediction of abiotic stress-responsive long non-coding RNAs in plants
- Implementation of machine learning in DNA barcoding for determining the plant family taxonomy
- Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode
- Unraveling Genetic Complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size
- Diffusion-Based Generative Network for de Novo Synthetic Promoter Design.
- DNA N-gram analysis framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences
- Comparison of classical Machine Learning-based algorithms to predict Triplex Forming Oligonucleotides
- Distribution rules of 8-mer spectra and characterization of evolution state in animal genome sequences
- Application of DNA barcode for the genetic analysis and identifying a May nuoc mo species in Quang Nam Province, Central Vietnam
- Genomic evolution of SARS-CoV-2 delta variants pre- and post-omicron emergence using alignment-free machine learning models
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