Cargando…
A pitfall for machine learning methods aiming to predict across cell types
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to perform well by effectively memorizing the average...
Autores principales: | Schreiber, Jacob, Singh, Ritambhara, Bilmes, Jeffrey, Noble, William Stafford |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678316/ https://www.ncbi.nlm.nih.gov/pubmed/33213499 http://dx.doi.org/10.1186/s13059-020-02177-y |
Ejemplares similares
-
Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples
por: Schreiber, Jacob, et al.
Publicado: (2020) -
Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome
por: Schreiber, Jacob, et al.
Publicado: (2020) -
Author Correction: Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome
por: Schreiber, Jacob, et al.
Publicado: (2021) -
Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data
por: Schreiber, Jacob, et al.
Publicado: (2020) -
A learned embedding for efficient joint analysis of millions of mass spectra
por: Bittremieux, Wout, et al.
Publicado: (2022)