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

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

Abstract 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...

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Auteurs principaux: Farideh Jalali-najafabadi, Michael Stadler, Nick Dand, Deepak Jadon, Mehreen Soomro, Pauline Ho, Helen Marzo-Ortega, Philip Helliwell, Eleanor Korendowych, Michael A. Simpson, Jonathan Packham, Catherine H. Smith, Jonathan N. Barker, Neil McHugh, Richard B. Warren, Anne Barton, John Bowes, BADBIR Study Group, BSTOP Study Group
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Medicine
R
Science
Q
Accès en ligne:https://doaj.org/article/ef9896df34ad40f89822e14d6ff1f794
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https://doaj.org/article/ef9896df34ad40f89822e14d6ff1f794

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