Evaluation of different approaches for missing data imputation on features associated to genomic data

Evaluation of different approaches for missing data imputation on features associated to genomic data

Abstract Background Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis. The data collection process can lead to different distribution, frequency, and structure of missing data po...

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Bibliographic Details
Main Authors: Ben Omega Petrazzini, Hugo Naya, Fernando Lopez-Bello, Gustavo Vazquez, Lucía Spangenberg
Format: Article
Language:English
Published: BMC 2021-09-01
Series:BioData Mining
Subjects:
Machine learning
imputation
missing data
genomics
pathogenic variants
Online Access:https://doi.org/10.1186/s13040-021-00274-7

Internet

https://doi.org/10.1186/s13040-021-00274-7

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