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• W2891784361 abstract "Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as unknown, as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data.We trained Random Forest and Artificial Neural Network using metagenomic sequences taxonomically classified into virus and non-virus classes. The algorithms achieved accuracies well beyond chance level, with area under ROC curve 0.79. Two codons (TCG and CGC) were found to have a particularly strong discriminative capacity.RSCU-based machine learning techniques applied to metagenomic sequencing data can help identify a large number of putative viral sequences and provide an addition to conventional methods for taxonomic classification." @default.
• W2891784361 created "2018-09-27" @default.
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• W2891784361 date "2018-09-24" @default.
• W2891784361 modified "2023-10-18" @default.
• W2891784361 title "Machine Learning for detection of viral sequences in human metagenomic datasets" @default.
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• W2891784361 doi "https://doi.org/10.1186/s12859-018-2340-x" @default.
• W2891784361 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6154907" @default.
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