Article ; Online: Host Trait Prediction from High-Resolution Microbial Features.
Methods in molecular biology (Clifton, N.J.)
2021 Volume 2242, Page(s) 185–202
Abstract: Predicting host traits from metagenomes presents new challenges that can be difficult to overcome for researchers without a strong background in bioinformatics and/or statistics. Profiling bacterial communities using shotgun metagenomics often leads to ... ...
Abstract | Predicting host traits from metagenomes presents new challenges that can be difficult to overcome for researchers without a strong background in bioinformatics and/or statistics. Profiling bacterial communities using shotgun metagenomics often leads to the generation of a large amount of data that cannot be used directly for training a model. In this chapter we provide a detailed description of how to build a working machine learning model based on taxonomic and functional features of bacterial communities inhabiting the lungs of cystic fibrosis patients. Models are built in the R environment by using different freely available machine learning algorithms. |
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MeSH term(s) | Bacteria/classification ; Bacteria/genetics ; Bacteria/isolation & purification ; Cystic Fibrosis/genetics ; Cystic Fibrosis/microbiology ; Cystic Fibrosis Transmembrane Conductance Regulator/genetics ; DNA, Bacterial/metabolism ; Databases, Genetic ; Gene Expression Profiling ; Genome, Bacterial ; Humans ; Lung/microbiology ; Machine Learning ; Metagenome ; Metagenomics ; Mutation ; Phylogeny ; Research Design ; Software ; Transcriptome ; Workflow |
Chemical Substances | CFTR protein, human ; DNA, Bacterial ; Cystic Fibrosis Transmembrane Conductance Regulator (126880-72-6) |
Language | English |
Publishing date | 2021-05-07 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 1940-6029 |
ISSN (online) | 1940-6029 |
DOI | 10.1007/978-1-0716-1099-2_12 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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