Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequil...

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Detalles Bibliográficos
Autores principales: Jalali-najafabadi, Farideh, Stadler, Michael, Dand, Nick, Jadon, Deepak, Soomro, Mehreen, Ho, Pauline, Marzo-Ortega, Helen, Helliwell, Philip, Korendowych, Eleanor, Simpson, Michael A., Packham, Jonathan, Smith, Catherine H., Barker, Jonathan N., McHugh, Neil, Warren, Richard B., Barton, Anne, Bowes, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640070/
https://www.ncbi.nlm.nih.gov/pubmed/34857774
http://dx.doi.org/10.1038/s41598-021-00854-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640070/
https://www.ncbi.nlm.nih.gov/pubmed/34857774
http://dx.doi.org/10.1038/s41598-021-00854-x

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