Revisiting the Problem of Missing Values in High-Dimensional Data and Feature Selection Effect
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63211-2_16
Reference39 articles.
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3. Lazar, C., Gatto, L., Ferro, M., Bruley, C., Burger, T.: Accounting for the multiple natures of missing values in label-free quantitative proteomics data sets to compare imputation strategies. J. Proteome Res. 15(4), 1116–1125 (2016)
4. Harris, L., Fondrie, W.E., Oh, S., Noble, W.S.: Evaluating proteomics imputation methods with improved criteria. J. Proteome Res. 22(11), 3427–3438 (2023)
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