Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data

BACKGROUND: Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additi...

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Detalles Bibliográficos
Autores principales: Frisby, Trevor S., Baker, Shawn J., Marçais, Guillaume, Hoang, Quang Minh, Kingsford, Carl, Langmead, Christopher J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017869/
https://www.ncbi.nlm.nih.gov/pubmed/33794760
http://dx.doi.org/10.1186/s12859-021-04096-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017869/
https://www.ncbi.nlm.nih.gov/pubmed/33794760
http://dx.doi.org/10.1186/s12859-021-04096-6

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