Missing-values imputation algorithms for microarray gene expression data

• Main Authors: Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris
• Other Authors: Bolón-Canedo, Verónica
• Format: Book Section
• Language: English; English; English
• Published: Humana Press 2019
• Subjects: Q Science (General); R Medicine (General); T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/25080/1/978-1-4939-9442-7_12; http://umpir.ump.edu.my/id/eprint/25080/2/66.Missing-Values%20Imputation%20Algorithms%20for%20Microarray%20Gene%20Expression%20Data.pdf; http://umpir.ump.edu.my/id/eprint/25080/3/66.1%20Missing-values%20imputation%20algorithms%20for%20microarray%20gene%20expression%20data.pdf; http://umpir.ump.edu.my/id/eprint/25080/; https://link.springer.com/protocol/10.1007/978-1-4939-9442-7_12; https://doi.org/10.1007/978-1-4939-9442-7_12
• Institution: Universiti Malaysia Pahang

In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al....