rSWeeP: A R/Bioconductor package deal with SWeeP sequences representation
Fernandes, D. R.; Kulik, M. G.; Machado, D. J. S.; Marchaukoski, J. N.; Pedrosa, F. O.; De Pierri, C. R.; Raittz, R. T.
Show abstract
The rSWeeP package is an R implementation of the SWeeP model, designed to handle Big Data. rSweeP meets to the growing demand for efficient methods of heuristic representation in the field of Bioinformatics, on platforms accessible to the entire scientific community. We explored the implementation of rSWeeP using a dataset containing 31,386 viral proteomes, performing phylogenetic and principal component analysis. As a case study we analyze the viral strains closest to the SARS-CoV, responsible for the current pandemic of COVID-19, confirming that rSWeeP can accurately classify organisms taxonomically. rSWeeP package is freely available at https://bioconductor.org/packages/release/bioc/html/rSWeeP.html.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study 96%
- GenomeBits insight into omicron and delta variants of coronavirus pathogen 96%
- SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants SARS-CoV-2 protein structure and sequence mutations: 95%
Similar papers in this journal
- HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences 95%
- HIHISIV: a database of gene expression in HIV and SIV host immune response 95%
- RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets 94%
Similar papers in this journal
- VIGA: an one-stop tool for eukaryotic Virus Identification and Genome Assembly from next-generation-sequencing data 94%
- A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data 94%
- Bioinformatics analysis and collection of protein post-translational modification sites in human viruses 94%
Similar papers in this journal
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 93%
- Viral miRNAs Confer Survival in Host Cells by Targeting Apoptosis Related Host Genes 93%
- Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.