Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses
High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically...
Main Authors: | Md. Nafis Ul Alam, Umar Faruq Chowdhury, Ruslan Kalendar |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500682/?tool=EBI |
Similar Items
-
Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses.
by: Md Nafis Ul Alam, et al.
Published: (2020-01-01) -
K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes
by: Simon Orozco-Arias, et al.
Published: (2021-05-01) -
Mining statistically-solid k-mers for accurate NGS error correction
by: Liang Zhao, et al.
Published: (2018-12-01) -
In Silico Estimation of the Abundance and Phylogenetic Significance of the Composite Oct4-Sox2 Binding Motifs within a Wide Range of Species
by: Arman Kulyyassov, et al.
Published: (2020-11-01) -
PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.
by: Jaroslav Bendl, et al.
Published: (2014-01-01)