Article
Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
Scientific reports - 2 Dec 2021
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
Abstract excerpt
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 disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this...
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