Scalable Classification of Organisms into a Taxonomy Using Hierarchical Supervised Learners
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Summary
A multi-level hierarchical classifier framework to automatically assign taxonomy labels to DNA sequences, utilizing an alignment-free approach called spectrum kernel method for feature extraction and showing that the proposed framework is more robust to mutations and noise in sequence data than the non-hierarchical classifiers.
- Type
- preprint
- Published
- 2020-02-04
- Cited by
- 4
- References
- 40
- Access
- Open access
- OpenAlex
- https://openalex.org/W3005374239
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:213260490
Keywords
Computer science, Classifier (UML), Artificial intelligence, Scalability, Machine learning
References
- Efficient alignment-free DNA barcode analytics
- Classification and Regression by randomForest
- Generating Accurate Rule Sets Without Global Optimization
- Logistic Model Trees
- DNA barcode analysis: a comparison of phylogenetic and statistical classification methods
- The Spectrum Kernel: A String Kernel for SVM Protein Classification
- Filling Gaps in Biodiversity Knowledge for Macrofungi: Contributions and Assessment of an Herbarium Collection DNA Barcode Sequencing Project
- DNA barcoding discriminates echinoderm species
- The neighbor-joining method: a new method for reconstructing phylogenetic trees.
- BLOG 2.0: a software system for character‐based species classification with DNA Barcode sequences. What it does, how to use it
- Kernel methods for predicting protein-protein interactions
- Biological identifications through DNA barcodes
- A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.
- Scalable Algorithms for String Kernels with Inexact Matching
- Supervised DNA Barcodes species classification: analysis, comparisons and results
- Rapid and accurate taxonomic classification of insect (class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier
- RipMC: RIPPER for Multiclass Classification
- Population size may shape the accumulation of functional mutations following domestication
- Species Identification Using Partial DNA Sequence: A Machine Learning Approach
- A new efficient method for analyzing fungi species using correlations between nucleotides
Cited by
- The potential for different computed tomography-based machine learning networks to automatically segment and differentiate pelvic and sacral osteosarcoma from Ewing’s sarcoma
- Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT
- Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions
- REVOLUTIONIZING DNA SPECIES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK
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